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'''[[Main Page|Home]] * Learning'''

[[FILE:Laurentius de Voltolina 001.jpg|border|right|thumb| Learning <ref>A depiction of the world's oldest continually operating university, the [https://en.wikipedia.org/wiki/University_of_Bologna University of Bologna], Italy, by Laurentius de Voltolina, second half of 14th century, [https://en.wikipedia.org/wiki/Learning Learning from Wikipedia]</ref> ]]

'''Learning''',<br/>
the process of acquiring new [[Knowledge|knowledge]] which involves synthesizing different types of [https://en.wikipedia.org/wiki/Information information]. [https://en.wikipedia.org/wiki/Machine_learning Machine learning] as aspect of computer chess programming deals with algorithms that allow the program to change its behavior based on data, which for instance occurs during [[Chess Game|game playing]] against a variety of opponents considering the final outcome and/or the game record for instance as history score chart indexed by ply. Related to Machine learning is [https://en.wikipedia.org/wiki/Evolutionary_computation evolutionary computation] and its sub-areas of [https://en.wikipedia.org/wiki/Genetic_algorithm genetic algorithms], and [[Genetic Programming|genetic programming]], that mimics the process of natural [https://en.wikipedia.org/wiki/Evolution evolution], as further mentioned in [[Automated Tuning|automated tuning]]. The process of learning often implies [https://en.wikipedia.org/wiki/Understanding understanding], [https://en.wikipedia.org/wiki/Perception perception] or [https://en.wikipedia.org/wiki/Reasoning reasoning]. So called [https://en.wikipedia.org/wiki/Rote_learning Rote learning] avoids understanding and focuses on [[Memory|memorization]]. [https://en.wikipedia.org/wiki/Inductive_reasoning Inductive] learning takes examples and generalizes rather than starting with existing knowledge. [https://en.wikipedia.org/wiki/Deductive_reasoning Deductive] learning takes abstract concepts to make sense of examples <ref>[http://www.beatthegmat.com/inductive-learning-vs-deductive-learning-t36273.html Inductive learning vs Deductive learning]</ref>.

=Learning inside a Chess Program=
Learning inside a chess program may address several disjoint issues. A [[Persistent Hash Table|persistent hash table]] remembers "important" positions from earlier games inside the [[Search|search]] with its [[Exact Score|exact score]] <ref>[[David Slate]] ('''1987'''). ''A Chess Program that uses its Transposition Table to Learn from Experience.'' [[ICGA Journal#10_2|ICCA Journal, Vol. 10, No. 2]]</ref>. Worse positions may be avoided in advance. [[Book Learning|Learning opening book moves]], that is appending successful novelties or modify the probability of already stored moves from the book based on the outcome of a game <ref>[[Robert Hyatt]] ('''1999'''). ''[http://www.craftychess.com/hyatt/learning.html Book Learning - a Methodology to Tune an Opening Book Automatically]''. [[ICGA Journal#22_1|ICCA Journal, Vol. 22, No. 1]]</ref>. Another application is learning [[Evaluation|evaluation]] weights of various features, f. i. [[Point Value|piece-]] <ref>[[Don Beal]], [[Martin C. Smith]] ('''1997'''). ''Learning Piece Values Using Temporal Differences''. [[ICGA Journal#20_3|ICCA Journal, Vol. 20, No. 3]]</ref> or [[Piece-Square Tables|piece-square]] <ref>[[Don Beal]], [[Martin C. Smith]] ('''1999'''). ''Learning Piece-Square Values using Temporal Differences.'' [[ICGA Journal#22_4|ICCA Journal, Vol. 22, No. 4]]</ref> values or [[Mobility|mobility]]. Programs may also learn to control search <ref>[[Yngvi Björnsson]], [[Tony Marsland]] ('''2001'''). ''Learning Search Control in Adversary Games''. [[Advances in Computer Games 9]], [http://www.ru.is/faculty/yngvi/pdf/BjornssonM01b.pdf pdf]</ref> or [[Time Management|time usage]] <ref>[[Levente Kocsis]], [[Jos Uiterwijk]], [[Jaap van den Herik]] ('''2000'''). ''[http://link.springer.com/chapter/10.1007/3-540-45579-5_11 Learning Time Allocation using Neural Networks]''. [[CG 2000]], [http://zaphod.aml.sztaki.hu/papers/kocsis-CG00.ps postscript]</ref>.

=Learning Paradigms=
There are three major learning [https://en.wikipedia.org/wiki/Paradigm paradigms], each corresponding to a particular abstract learning task. These are [https://en.wikipedia.org/wiki/Supervised_learning supervised learning], [https://en.wikipedia.org/wiki/Unsupervised_learning unsupervised learning] and [[Reinforcement Learning|reinforcement learning]]. Usually any given type of [[Neural Networks|neural network]] architecture can be employed in any of those tasks.

==Supervised Learning==
Supervised learning is learning from examples provided by a knowledgable external supervisor. In machine learning, supervised learning is a technique for deducing a function from training data. The training data consist of pairs of input objects and desired outputs, f.i. in computer chess a sequence of positions associated with the outcome of a game <ref>[http://www.aihorizon.com/essays/generalai/supervised_unsupervised_machine_learning.htm AI Horizon: Machine Learning, Part II: Supervised and Unsupervised Learning]</ref> .

==Unsupervised Learning==
Unsupervised machine learning seems much harder: the goal is to have the computer learn how to do something that we don't tell it how to do. The learner is given only unlabeled examples, f. i. a sequence of positions of a running game but the final result (still) unknown. A form of reinforcement learning can be used for unsupervised learning, where an [https://en.wikipedia.org/wiki/Intelligent_agent agent] bases its actions on the previous rewards and punishments without necessarily even learning any information about the exact ways that its actions affect the world. [https://en.wikipedia.org/wiki/Data_clustering Clustering] is another method of unsupervised learning.

==Reinforcement Learning==
''see main page [[Reinforcement Learning]]''

Reinforcement learning is defined not by characterizing learning methods, but by characterizing a learning problem. Reinforcement learning is learning what to do - how to map situations to actions - so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them. The reinforcement learning problem is deeply indebted to the idea of [https://en.wikipedia.org/wiki/Markov_decision_process Markov decision processes] (MDPs) from the field of [https://en.wikipedia.org/wiki/Optimal_control optimal control].

=Learning Topics=
* [[Automated Tuning]]
* [[Bayesian Networks]]
* [[Book Learning]]
* [[CHREST]]
* [[Deep Learning]]
* [[EPAM]]
* [[Genetic Programming]]
* [[Neural Networks]]
* [[Pattern Learning]]
* [[Pattern Recognition]]
* [[Persistent Hash Table]]
* [[Planning]]
* [[Reinforcement Learning]]
* [[Temporal Difference Learning]]
<span id="Programs"></span>
=Programs=
* [[AlphaZero]]
* [[Alexs]]
* [[Bebe]]
* [[Blondie25]]
* [[ChessMaps]]
* [[Chessterfield]]
* [[CHUMP]]
* [[Deep Pink]]
* [[Falcon]]
* [[Giraffe]]
* [[Golch]]
* [[KnightCap]]
* [[LCZero]]
* [[Meep]]
* [[Morph]]
* [[NeuroChess]]
* [[RomiChess]]
* [[Octavius]]
* [[SAL]]
* [[Stoofvlees]]
* [[TDChess]]
* [[Tempo (engine)|Tempo]]
* [[Winter]]

=See also=
* [[Cognition]]
* [[Dynamic Programming]]
* [[Knowledge]]
* [[Memory]]
* [[Psychology]]
* [[Robots]]
* [[Trial and Error]]

=Selected Publications=
<ref>[http://satirist.org/learn-game/lists/papers.html online papers] from [http://satirist.org/learn-game/ Machine Learning in Games] by [[Jay Scott]]</ref>
==1940 ...==
* [https://en.wikipedia.org/wiki/Walter_Pitts Walter Pitts] ('''1942'''). ''[http://link.springer.com/article/10.1007%2FBF02477942 Some observations on the simple neuron circuit]''. [http://link.springer.com/journal/11538 Bulletin of Mathematical Biology], Vol. 4, No. 3
* [https://en.wikipedia.org/wiki/Warren_Sturgis_McCulloch Warren S. McCulloch], [https://en.wikipedia.org/wiki/Walter_Pitts Walter Pitts] ('''1943'''). ''[http://link.springer.com/article/10.1007%2FBF02478259 A Logical Calculus of the Ideas Immanent in Nervous Activity]''. [http://link.springer.com/journal/11538 Bulletin of Mathematical Biology], Vol. 5, No. 1
* [https://en.wikipedia.org/wiki/Donald_O._Hebb Donald O. Hebb] ('''1949'''). ''[https://en.wikipedia.org/wiki/The_Organization_of_Behavior The Organization of Behavior]''. [https://en.wikipedia.org/wiki/John_Wiley_%26_Sons Wiley & Sons]
==1950 ...==
* [[Mathematician#SCKleene|Stephen C. Kleene]] ('''1951''') ''Representation of Events in Nerve Nets and Finite Automata''. RM-704, [https://en.wikipedia.org/wiki/RAND_Corporation RAND paper], [http://www.rand.org/content/dam/rand/pubs/research_memoranda/2008/RM704.pdf pdf], reprinted in
: [[Claude Shannon]], [[John McCarthy]] (eds.) ('''1956'''). ''Automata Studies''. [http://press.princeton.edu/math/series/amh.html Annals of Mathematics Studies], No. 34
* [http://adsabs.harvard.edu/cgi-bin/nph-abs_connect?return_req=no_params&author=Richards,%20Paul%20I.&db_key=GEN Paul I. Richards] ('''1951'''). ''Machines which can learn''. [https://en.wikipedia.org/wiki/American_Scientist American Scientist], 39:711-716
* [http://adsabs.harvard.edu/cgi-bin/nph-abs_connect?return_req=no_params&author=Richards,%20Paul%20I.&db_key=GEN Paul I. Richards] ('''1952'''). ''On Game Learning Machines''. [https://en.wikipedia.org/wiki/The_Scientific_Monthly The Scientific Monthly], Vol. 74, No. 4, April 1952
* [[Alan Turing]] ('''1953'''). ''Chess''. part of the collection ''Digital Computers Applied to Games'' in [https://en.wikipedia.org/wiki/B._V._Bowden,_Baron_Bowden Bertram Vivian Bowden] (editor), [http://www.computinghistory.org.uk/cgi-bin/sitewise.pl?act=det&p=10719 Faster Than Thought], a symposium on digital computing machines, reprinted 1988 in [[Computer Chess Compendium]], reprinted in
: [[Alan Turing]], [https://en.wikipedia.org/wiki/Jack_Copeland Jack Copeland] (editor) ('''2004'''). ''The Essential Turing, Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life plus The Secrets of Enigma''. [https://en.wikipedia.org/wiki/Oxford_University_Press Oxford University Press], [http://www.amazon.com/Essential-Turing-Philosophy-Artificial-Intelligence/dp/0198250800/ref=sr_1_1?s=books&ie=UTF8&qid=1324659595&sr=1-1 amazon], [http://books.google.com/books?id=RSkxnKlv1D4C&lpg=PP882&ots=VOWmiIm_lD&dq=Turochamp%2C%20chess&pg=PP881#v=onepage&q&f=true google books]
* [[Marvin Minsky]] ('''1954'''). ''Neural Nets and the Brain Model Problem''. Ph.D. dissertation, [https://en.wikipedia.org/wiki/Princeton_University Princeton University]
==1955 ...==
* [[John von Neumann]] ('''1956'''). ''Probabilistic Logic and the Synthesis of Reliable Organisms From Unreliable Components''. in
: [[Claude Shannon]], [[John McCarthy]] (eds.) ('''1956'''). ''Automata Studies''. [http://press.princeton.edu/math/series/amh.html Annals of Mathematics Studies], No. 34, [http://www.dna.caltech.edu/courses/cs191/paperscs191/VonNeumann56.pdf pdf]
* [https://en.wikipedia.org/wiki/Frank_Rosenblatt Frank Rosenblatt] ('''1957'''). ''The Perceptron - a Perceiving and Recognizing Automaton''. Report 85-460-1, Cornell Aeronautical Laboratory <ref>[http://csis.pace.edu/~ctappert/srd2011/rosenblatt-contributions.htm Rosenblatt's Contributions]</ref>
* [http://www.purbeckradar.org.uk/biography/uttley_albert_m.htm Albert M. Uttley] ('''1959'''). ''[http://rmp.aps.org/abstract/RMP/v31/i2/p546_1 Imitation of Pattern Recognition and Trial-and-error Learning in a Conditional Probability Computer]''. [https://en.wikipedia.org/wiki/Reviews_of_Modern_Physics Reviews of Modern Physics], Vol. 31, April 1959, pp. 546-548 <ref>[https://en.wikipedia.org/wiki/Ratio_Club Ratio Club from Wikipedia]</ref> <ref>[https://en.wikipedia.org/wiki/Royal_Radar_Establishment Royal Radar Establishment from Wikipedia]</ref>
* [[Arthur Samuel]] ('''1959'''). ''[http://domino.watson.ibm.com/tchjr/journalindex.nsf/600cc5649e2871db852568150060213c/39a870213169f45685256bfa00683d74!OpenDocument Some Studies in Machine Learning Using the Game of Checkers]''. IBM Journal July 1959 » [[Checkers]]
* [[Edward Feigenbaum]] ('''1959'''). ''[http://www.rand.org/pubs/papers/P1817.html An Information Processing Theory of Verbal Learning]''. [https://en.wikipedia.org/wiki/RAND_Corporation RAND Paper]
==1960 ...==
* [[Edward Feigenbaum]] ('''1960'''). ''Information Theories of Human Verbal Learning''. Ph.D. thesis, [[Carnegie Mellon University]], advisor [[Herbert Simon]]
* [[Edward Feigenbaum]] ('''1961'''). ''[http://dl.acm.org/citation.cfm?id=1460704 The Simulation of Verbal Learning Behavior]''. Proceedings Western Joint Conference, Vol. 19
* [[Edward Feigenbaum]], [[Herbert Simon]] ('''1961'''). ''Performance of a Reading Task by an Elementary Perceiving and Memorizing Program''. [https://en.wikipedia.org/wiki/RAND_Corporation RAND Paper], [http://www.rand.org/content/dam/rand/pubs/papers/2008/P2358.pdf pdf]
* [[Donald Michie]] ('''1961'''). ''Trial and Error''. Penguin Science Survey, [http://staff.science.uva.nl/~aldersho/GameProgramming/Papers/MichieMENACE.pdf pdf]
* [[Edward Feigenbaum]], [[Herbert Simon]] ('''1962'''). ''A Theory of the Serial Position Effect''. [https://en.wikipedia.org/wiki/British_Journal_of_Psychology#Journals British Journal of Psychology], Vol. 53, 307-32, [http://www.rand.org/pubs/papers/2008/P2375.pdf pdf]
* [https://en.wikipedia.org/wiki/Earl_B._Hunt Earl B. Hunt] ('''1962'''). ''Concept Learning: An Information Processing Problem''. Wiley. [http://books.google.com/books/about/Concept_learning.html?id=zUhAAAAAIAAJ google books]
* [https://en.wikipedia.org/wiki/Frank_Rosenblatt Frank Rosenblatt] ('''1962'''). ''[http://catalog.hathitrust.org/Record/000203591 Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms]''. Spartan Books
* [[Allen Newell]] ('''1963'''). ''Learning, Generality and Problem Solving''. [http://www.textfiles.com/bitsavers/pdf/rand/ipl/RM-3285-PR_Learning_Generality_And_Problem_Solving_Feb63.pdf Memorandum RM-3285-1-PR pdf]
* [[Herbert Simon]], [[Edward Feigenbaum]] ('''1964'''). ''An Information-processing Theory of Some Effects of Similarity, Familiarization, and Meaningfulness in Verbal Learning''. Journal of Verbal Learning and Verbal Behavior, Vol. 3, No. 5, [https://saltworks.stanford.edu/assets/zp668jb3733.pdf pdf]
==1965 ...==
* [[James R. Slagle]] ('''1965'''). ''A multipurpose Theorem Proving Heuristic Program that learns''. [http://www.bibliopolis.com/main/books/caliban_0032271.html IFIP Congress 65, Vol. 2]
* [[Donald Michie]] ('''1966'''). ''Game Playing and Game Learning Automata.'' Advances in Programming and Non-Numerical Computation, [https://en.wikipedia.org/wiki/Leslie_Fox Leslie Fox] (ed.), pp. 183-200. Oxford, Pergamon. » Includes Appendix: ''Rules of SOMAC'' by [[John Maynard Smith]], introduces [https://en.wikipedia.org/wiki/Expectiminimax_tree Expectiminimax tree] <ref>see [[Helmut Richter#Swapoff|Swap-off]] by [[Helmut Richter]]</ref>
* [[Thomas A. Throop]] ('''1966'''). ''[http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=AD0686760 Thoughts on the Development of Computer Learning Programs]''. [https://en.wikipedia.org/wiki/Defense_Technical_Information_Center Defense Technical Information Center]
* [[Arnold K. Griffith]] ('''1966'''). ''[http://dspace.mit.edu/handle/1721.1/5896#files-area A new Machine-Learning Technique applied to the Game of Checkers]''. [[Massachusetts Institute of Technology|MIT]], [https://en.wikipedia.org/wiki/MIT_Computer_Science_and_Artificial_Intelligence_Laboratory#Project_MAC Project MAC], MAC-M-293
* [[Arthur Samuel]] ('''1967'''). ''Some Studies in Machine Learning. Using the Game of Checkers. II-Recent Progress''. [https://researcher.ibm.com/researcher/files/us-beygel/samuel-checkers.pdf pdf]
* [[Marvin Minsky]], [[Mathematician#SPapert|Seymour Papert]] ('''1969'''). ''[https://en.wikipedia.org/wiki/Perceptrons_%28book%29 Perceptrons]''. <ref>[https://en.wikipedia.org/wiki/AI_winter#The_abandonment_of_connectionism_in_1969 The abandonment of connectionism in 1969 - Wikipedia]</ref> <ref>[https://en.wikipedia.org/wiki/Frank_Rosenblatt Frank Rosenblatt] ('''1962'''). ''[http://catalog.hathitrust.org/Record/000203591 Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms]''. Spartan Books</ref>
==1970 ...==
* [[Albert Zobrist]] ('''1970'''). ''A Pattern Recognition Program which uses a Geometry-Preserving Representation of Features''. Technical Report #85, [http://www.cs.wisc.edu/techreports/1970/TR85.pdf pdf]
* [[Mathematician#VNVapnik|Vladimir Vapnik]], [[Mathematician#AChervonenkis|Alexey Chervonenkis]] ('''1971'''). ''[http://epubs.siam.org/doi/abs/10.1137/1116025 On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities]''. [https://en.wikipedia.org/wiki/Theory_of_Probability_and_Its_Applications Theory of Probability and its Applications], Vol. 16, No. 2
* [[A. Harry Klopf]] ('''1972'''). ''Brain Function and Adaptive Systems - A Heterostatic Theory''. Air Force Cambridge Research Laboratories, Special Reports, No. 133, [http://www.dtic.mil/dtic/tr/fulltext/u2/742259.pdf pdf]
* [[Marvin Minsky]], [[Mathematician#SPapert|Seymour Papert]] ('''1972'''). ''[https://en.wikipedia.org/wiki/Perceptrons_%28book%29 Perceptrons: An Introduction to Computational Geometry]''. [https://en.wikipedia.org/wiki/MIT_Press The MIT Press], 2nd edition with corrections
* [[Herbert Simon]], [[Kevin J. Gilmartin]] ('''1973'''). ''A Simulation of Memory for Chess Positions''. Cognitive Psychology, Vol. 5, pp. 29-46. [http://www.cs.wright.edu/~snarayan/isis/pdf/group5one.pdf pdf]
* [[Arnold K. Griffith]] ('''1974'''). ''[http://www.sciencedirect.com/science/article/pii/0004370274900277 A Comparison and Evaluation of Three Machine Learning Procedures as Applied to the Game of Checkers]''. [https://en.wikipedia.org/wiki/Artificial_Intelligence_%28journal%29 Artificial Intelligence], Vol. 5, No. 2 » [[Checkers]]
==1975 ...==
* [[Jacques Pitrat]] ('''1976'''). ''A Program to Learn to Play Chess.'' Pattern Recognition and Artificial Intelligence, pp. 399-419. Academic Press Ltd. London, UK. ISBN 0-12-170950-7.
* [[Jacques Pitrat]] ('''1976'''). ''Realization of a Program Learning to Find Combinations at Chess.'' Computer Oriented Learning Processes (ed. J. Simon). Noordhoff, Groningen, The Netherlands.
* [[Pericles Negri]] ('''1977'''). ''Inductive Learning in a Hierarchical Model for Representing Knowledge in Chess End Games''. [http://www.mli.gmu.edu/papers/69-78/77-2.pdf pdf]
* [[Ryszard Michalski]], [[Pericles Negri]] ('''1977'''). ''An experiment on inductive learning in chess endgames''. [http://www.doc.ic.ac.uk/~shm/MI/mi8.html Machine Intelligence 8], [http://www.mli.gmu.edu/papers/69-78/77-1.pdf pdf]
* [[Boris Stilman]] ('''1977'''). ''The Computer Learns''. in ''1976 US Computer Chess Championship'', by [[David Levy]], Computer Science Press, Woodland Hills, CA, pp. 83-90
* [[Richard Sutton]] ('''1978'''). ''Single channel theory: A neuronal theory of learning''. Brain Theory Newsletter 3, No. 3/4, pp. 72-75.
* [[Ross Quinlan]] ('''1979'''). ''Discovering Rules by Induction from Large Collections of Examples''. Expert Systems in the Micro-electronic Age, pp. 168-201. Edinburgh University Press (Introducing ID3)
==1980 ...==
* [[Sarah E. Goldin]], [http://www.linkedin.com/pub/phil-klahr/8/72b/676/de Philip Klahr] ('''1981'''). ''[http://dl.acm.org/citation.cfm?id=1623197 Learning and Abstraction in Simulation]''. [http://www.informatik.uni-trier.de/~ley/db/conf/ijcai/ijcai81.html#GoldinK81 IJCAI 1981], [http://ijcai.org/Past%20Proceedings/IJCAI-81-VOL%201/PDF/042.pdf pdf]
* [[Paul E. Utgoff]], [[Tom Mitchell]] ('''1982'''). ''Acquisition of Appropriate Bias for Inductive Concept Learning''. [http://dblp.uni-trier.de/db/conf/aaai/aaai82.html#UtgoffM82 AAAI 1982], [https://www.aaai.org/Papers/AAAI/1982/AAAI82-099.pdf pdf]
* [[A. Harry Klopf]] ('''1982'''). ''The Hedonistic Neuron: A Theory of Memory, Learning, and Intelligence''. Hemisphere Publishing Corporation, [[University of Michigan]]
* [[Alen Shapiro]], [[Tim Niblett]] ('''1982'''). ''Automatic Induction of Classification Rules for Chess End game.'' [[Advances in Computer Chess 3]]
* [[Thomas Nitsche]] ('''1982'''). ''A Learning Chess Program.'' [[Advances in Computer Chess 3]]
* [[Ryszard Michalski]], [[Jaime Carbonell]], [[Tom Mitchell]] ('''1983'''). ''Machine Learning: An Artificial Intelligence Approach''. Tioga Publishing Company, ISBN 0-935382-05-4. [http://books.google.com/books?id=KlNQAAAAMAAJ&q=isbn:0935382054&dq=isbn:0935382054&hl=de&ei=JE4-TtymDInUsgbmm_kG&sa=X&oi=book_result&ct=result&resnum=1&ved=0CCkQ6AEwAA google books]
* [[Ross Quinlan]] ('''1983'''). ''Learning efficient classification procedures and their application to chess end games''. In Machine Learning: An Artificial Intelligence Approach, pages 463–482. Tioga, Palo Alto
* [[Alen Shapiro]] ('''1983'''). ''The Role of Structured Induction in Expert Systems''. [[University of Edinburgh]], Machine Intelligence Research Unit (Ph.D. thesis)
* [[Edward Feigenbaum]], [[Herbert Simon]] ('''1984'''). ''[http://www.sciencedirect.com/science/article/pii/S0364021384800051 EPAMlike models of recognition and learning]''. [https://en.wikipedia.org/wiki/Cognitive_Science_Society Cognitive Science], Vol. 8, 305-336, [http://csjarchive.cogsci.rpi.edu/1984v08/i04/p0305p0336/MAIN.PDF pdf]
* [[Mathematician#JELaird|John E. Laird]], [[Paul S. Rosenbloom]], [[Allen Newell]] ('''1984'''). ''Towards Chunking as a General Learning Mechanism''. [[AAAI|AAAI 1984]]
* [[Albrecht Heeffer]] ('''1984'''). ''Automated Acquisition on Concepts for the Description of Middle-game Positions in Chess''. [https://en.wikipedia.org/wiki/Turing_Institute Turing Institute], [https://en.wikipedia.org/wiki/Glasgow Glasgow], [https://en.wikipedia.org/wiki/Scotland Scotland], TIRM-84-005
* [[Paul E. Utgoff]] ('''1984'''). ''Shift of Bias for Inductive Concept Learning''. Ph.D. thesis, [https://en.wikipedia.org/wiki/Rutgers%E2%80%93New_Brunswick Rutgers University, New Brunswick]
* [[Mathematician#LValiant|Leslie Valiant]] ('''1984'''). ''A Theory of the Learnable''. [[ACM#Communications|Communications of the ACM]], Vol. 27, No. 11, [http://web.mit.edu/6.435/www/Valiant84.pdf pdf]
==1985 ...==
* [[Tony Marsland]] ('''1985'''). ''Evaluation-Function Factors''. [[ICGA Journal#8_2|ICCA Journal, Vol. 8, No. 2]], [http://webdocs.cs.ualberta.ca/~tony/OldPapers/evaluation.pdf pdf]
* [[Albrecht Heeffer]] ('''1985'''). ''Validating Concepts from Automated Acquisition Systems''. [[Conferences#IJCAI|IJCAI 85]], [http://ijcai.org/Past%20Proceedings/IJCAI-85-VOL1/PDF/118.pdf pdf]
* [[Hans Berliner]] ('''1985'''). ''Goals, Plans, and Mechanisms: Non-symbolically in an Evaluation Surface.'' Presentation at Evolution, Games, and Learning, Center for Nonlinear Studies, [[Los Alamos National Laboratory]], May 21.
* [[Ryszard Michalski]], [[Jaime Carbonell]], [[Tom Mitchell]] ('''1985'''). ''Machine Learning: An Artificial Intelligence Approach''. Morgan Kaufmann, ISBN 0-934613-09-5. [http://books.google.com/books?id=TWzuUd5gsnkC&dq=isbn%3A0935382054&hl=de&source=gbs_book_other_versions google books]
'''1986'''
* [[Steven Skiena]] ('''1986'''). ''An Overview of Machine Learning in Chess.'' [[ICGA Journal#9_1|ICCA Journal, Vol. 9, No. 1]]
* [[Jens Christensen]], [[Richard Korf]] ('''1986'''). ''A Unified Theory of Heuristic Evaluation functions and Its Applications to Learning.'' Proceedings of the AAAI-86, pp. 148-152, [http://www.aaai.org/Papers/AAAI/1986/AAAI86-023.pdf pdf].
* [[Ryszard Michalski]], [[Jaime Carbonell]], [[Tom Mitchell]] ('''1986'''). ''Machine Learning: An Artificial Intelligence Approach, Volume II''. Morgan Kaufmann, ISBN 0-934613-00-1. [http://books.google.com/books?id=f9RylgKpHZsC&dq=isbn%3A0935382054&hl=de&source=gbs_book_other_versions google books]
* [[Tom Mitchell]], [[Jaime Carbonell]], [[Ryszard Michalski]] ('''1986'''). ''[http://link.springer.com/book/10.1007/978-1-4613-2279-5 Machine Learning: A Guide to Current Research]''. The Kluwer International Series in Engineering and Computer Science, Vol. 12
* [[Ivan Bratko]], [[Igor Kononenko]] ('''1986'''). ''Learning Rules from Incomplete and Noisy Data.'' Proceedings Unicom Seminar on the Scope of Artificial Intelligence in Statistics. Technical Press
'''1987'''
* [[David Slate]] ('''1987'''). ''A Chess Program that uses its Transposition Table to Learn from Experience.'' [[ICGA Journal#10_2|ICCA Journal, Vol. 10, No. 2]]
* [[Ronald L. Rivest]] ('''1987'''). ''Learning Decision Lists''. Machine Learning 2,3, [http://people.csail.mit.edu/rivest/Rivest-DecisionLists.pdf pdf 2001]
* [[Gerald Tesauro]], [[Terrence J. Sejnowski]] ('''1987'''). ''A 'Neural' Network that Learns to Play Backgammon''. [http://www.informatik.uni-trier.de/~ley/db/conf/nips/nips1987.html#TesauroS87 NIPS 1987]
* [[Alen Shapiro]] ('''1987'''). ''Structured Induction in Expert Systems''. Turing Institute Press in association with Addison-Wesley Publishing Company, Workingham, UK
* [[Alberto Maria Segre]] ('''1987'''). ''On the Operationality/Generality Trade-off in Explanation-based Learning''. [http://dblp.uni-trier.de/db/conf/ijcai/ijcai87.html IJCAI 1987], [http://ijcai.org/Past%20Proceedings/IJCAI-87-VOL1/PDF/049.pdf pdf]
* [[Alberto Maria Segre]] ('''1987'''). ''Explanation-Based Learning of Generalized Robot Assembly Plans''. Ph.D. thesis, [[University of Illinois at Urbana-Champaign]], Advisor: [http://www.ece.illinois.edu/directory/profile.asp?mrebl Gerald Francis DeJong, II]
* [[Eric B. Baum]], [https://en.wikipedia.org/wiki/Frank_Wilczek Frank Wilczek] ('''1987'''). ''[http://papers.nips.cc/paper/3-supervised-learning-of-probability-distributions-by-neural-networks Supervised Learning of Probability Distributions by Neural Networks]''. [http://papers.nips.cc/book/neural-information-processing-systems-1987 NIPS 1987]
'''1988'''
* [[Bruce Abramson]] ('''1988'''). ''Learning Expected-Outcome Evaluators in Chess.'' Proceedings of the 1988 AAAI Spring Symposium Series: Computer Game Playing, 26-28.
* [[Richard Sutton]] ('''1988'''). ''Learning to Predict by the Methods of Temporal Differences''. [https://en.wikipedia.org/wiki/Machine_Learning_%28journal%29 Machine Learning], Vol. 3, No. 1, [https://webdocs.cs.ualberta.ca/~sutton/papers/sutton-88-with-erratum.pdf pdf]
* [[David E. Goldberg]], [[Mathematician#Holland|John H. Holland]] ('''1988'''). ''[http://www.springerlink.com/content/rw3572714v41q507/ Genetic Algorithms and Machine Learning]''. [https://en.wikipedia.org/wiki/Machine_Learning_%28journal%29 Machine Learning], Vol. 3
* [[Mathematician#KADeJong|Kenneth A. De Jong]], [[Mathematician#ACSchultz|Alan C. Schultz]] ('''1988'''). ''Using Experience-Based Learning in Game Playing''. Proceedings of the Fifth International Machine Learning Conference, [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.5381 CiteSeerX] » [[Othello]]
* [[Kai-Fu Lee]], [[Sanjoy Mahajan]] ('''1988'''). ''[http://www.sciencedirect.com/science/article/pii/0004370288900768 A Pattern Classification Approach to Evaluation Function Learning]''. [https://en.wikipedia.org/wiki/Artificial_Intelligence_%28journal%29 Artificial Intelligence], Vol. 36, No. 1
* [[Paul E. Utgoff]] ('''1988'''). ''[http://dl.acm.org/citation.cfm?id=896712 ID5: An incremental ID3]''. [http://dblp.uni-trier.de/db/conf/icml/ml1988.html#Utgoff88 ML 1988]
'''1989'''
* [[Robert Levinson]] ('''1989'''). ''A Self-Learning, Pattern-Oriented Chess Program''. [[ICGA Journal#12_4|ICCA Journal, Vol. 12, No. 4]]
* [[Bruce Abramson]] ('''1989'''). ''On Learning and Testing Evaluation Functions.'' Proceedings of the Sixth Israeli Conference on Artificial Intelligence, 1989, 7-16.
* [[Eric Wefald]], [[Stuart Russell]] ('''1989'''). ''[http://portal.acm.org/citation.cfm?id=102248 Adaptive Learning of Decision-Theoretic Search Control Knowledge]''. In Proceedings of the Sixth International Workshop on Machine Learning. Ithaca, NY: Morgan Kaufmann
* [[Stephen Muggleton]], [[Michael Bain]], [[Jean Hayes Michie]], [[Donald Michie]] ('''1989'''). ''An Experimental Comparison of Human and Machine Learning Formalisms''. [http://www.informatik.uni-trier.de/~ley/db/conf/icml/ml1989.html#MuggletonBMM89 6. ML 1989], [http://www.doc.ic.ac.uk/~shm/Papers/ml6paper.pdf pdf]
* [[Eric B. Baum]] ('''1989'''). ''[http://www.mitpressjournals.org/doi/abs/10.1162/neco.1989.1.2.201#.VfGX0JdpluM A Proposal for More Powerful Learning Algorithms]''. [https://en.wikipedia.org/wiki/Neural_Computation_%28journal%29 Neural Computation], Vol. 1, No. 2
* [[Susan L. Epstein]] ('''1989'''). ''The Intelligent Novice - Learning to Play Better''. [[1st Computer Olympiad#Workshop|Heuristic Programming in Artificial Intelligence 1]]
* [[Chris Watkins]] ('''1989'''). ''Learning from Delayed Rewards''. Ph.D. thesis, [https://en.wikipedia.org/wiki/University_of_Cambridge Cambridge University], [http://www.cs.rhul.ac.uk/~chrisw/new_thesis.pdf pdf]
==1990 ...==
* [[Richard Sutton]], [[Andrew Barto]] ('''1990'''). ''Time Derivative Models of Pavlovian Reinforcement''. Learning and Computational Neuroscience: Foundations of Adaptive Networks: 497-537.
* [[Bruce Abramson]] ('''1990'''). ''On Learning and Testing Evaluation Functions.'' Journal of Experimental and Theoretical Artificial Intelligence 2: 241-251.
* [[Tony Scherzer]], [[Linda Scherzer]], [[Dean Tjaden]] ('''1990'''). ''Learning in Bebe.'' [[Computers, Chess, and Cognition]] » [[BeBe#Award|Mephisto Best-Publication Award]]
* [[Yves Kodratoff]], [[Ryszard Michalski]] ('''1990'''). ''Machine Learning: An Artificial Intelligence Approach, Volume III''. Morgan Kaufmann, ISBN 1-55860-119-8. [http://books.google.com/books?id=UDqCeuwVkkcC&dq=isbn%3A0935382054&hl=de&source=gbs_book_other_versions google books]
* [[Michèle Sebag]] ('''1990'''). ''A symbolic-numerical approach for supervised learning from examples and rules''. Ph.D. thesis, [https://en.wikipedia.org/wiki/Paris_Dauphine_University Paris Dauphine University]
'''1991'''
* [[Robert Schapire]] ('''1991'''). ''The Design and Analysis of Efficient Learning Algorithms''. Ph.D. thesis, [[Massachusetts Institute of Technology]], supervisor [[Ronald L. Rivest]], [http://www.dtic.mil/dtic/tr/fulltext/u2/a231888.pdf pdf]
* [[Gerhard Mehlsam]], [[Hermann Kaindl]], [[Wilhelm Barth]] ('''1991'''). ''Feature Construction During Tree Learning''. [http://137.226.34.227/dblp/db/conf/ki/gwai91.html GWAI 1991]: 50-61.
* [[Alex van Tiggelen]] ('''1991'''). ''Neural Networks as a Guide to Optimization - The Chess Middle Game Explored''. [[ICGA Journal#14_3|ICCA Journal, Vol. 14, No. 3]]
* [[William Tunstall-Pedoe]] ('''1991'''). ''Genetic Algorithms Optimizing Evaluation Functions''. [[ICGA Journal#14_3|ICCA Journal, Vol. 14, No. 3]]
* [[Tony Scherzer]], [[Linda Scherzer]], [[Dean Tjaden]] ('''1991'''). ''Learning in Bebe.'' [[ICGA Journal#14_4|ICCA Journal, Vol. 14, No. 4]]
* [[Steven Walczak]] ('''1991'''). ''Predicting Actions from Induction on Past Performance''. Proceedings of the 8th International Workshop on Machine Learning , pp. 275-279. Morgan Kaufmann
* [[Paul E. Utgoff]], [[Jeffery A. Clouse]] ('''1991'''). ''[http://scholarworks.umass.edu/cs_faculty_pubs/193/ Two Kinds of Training Information for Evaluation Function Learning]''. [https://en.wikipedia.org/wiki/University_of_Massachusetts_Amherst University of Massachusetts, Amherst], Proceedings of the [[AAAI]] 1991
* [[Byoung-Tak Zhang]], [[Gerd Veenker]] ('''1991'''). ''[http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=170480&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D170480 Neural networks that teach themselves through genetic discovery of novel examples]''. [http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000500 IEEE International Joint Conference on Neural Networks]
'''1992'''
* [[Miroslav Kubat]] ('''1992'''). ''Introduction to Machine Learning''. [http://dblp.uni-trier.de/db/conf/ac/ai1992.html#Kubat92 Advanced Topics in Artificial Intelligence 1992]
* [[Michael Bain]] ('''1992'''). ''Learning optimal chess strategies.'' Proc. Intl. Workshop on Inductive Logic Programming (ed. [[Stephen Muggleton]]), Institute for New Generation Computer Technology, Tokyo, Japan.
* [[Eduardo F. Morales]] ('''1992'''). ''First-Order Induction of Patterns in Chess''. Ph.D. Thesis, The Turing Institute, [https://en.wikipedia.org/wiki/University_of_Strathclyde University of Strathclyde], [https://en.wikipedia.org/wiki/Glasgow Glasgow]
* [[Eduardo F. Morales]] ('''1992'''). ''Learning Chess Patterns''. Inductive Logic Programming (ed. [[Stephen Muggleton]]), Academic Press, The Apic Series, London, UK
* [[Gerald Tesauro]] ('''1992'''). ''Temporal Difference Learning of Backgammon Strategy''. [http://www.informatik.uni-trier.de/~ley/db/conf/icml/ml1992.html#Tesauro92 ML 1992]
* [[Chris Watkins]], [[Peter Dayan]] ('''1992'''). ''[http://www.gatsby.ucl.ac.uk/~dayan/papers/wd92.html Q-learning]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 2
* [[Gerald Tesauro]] ('''1992'''). ''[http://dl.acm.org/citation.cfm?id=139616 Practical Issues in Temporal Difference Learning]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 3-4
* [[Manuela Veloso]] ('''1992'''). ''[http://search.library.cmu.edu/vufind/Record/421096 Learning by Analogical Reasoning in General Purpose Problem Solving]''. Ph.D. thesis, [[Carnegie Mellon University]], advisor [[Jaime Carbonell]]
'''1993'''
* [[Michael Gherrity]] ('''1993'''). ''A Game Learning Machine''. Ph.D. Thesis, [http://de.wikipedia.org/wiki/University_of_California,_San_Diego University of California, San Diego], [http://www.gherrity.org/thesis.ps.gz zipped ps]
* [[Shaul Markovitch]], [http://www.cs.huji.ac.il/labs/danss/Fairplay/ Yaron Sella] ('''1993'''). ''Learning of Resource Allocation Strategies for Game Playing'', The proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambery, France. [http://www.cs.technion.ac.il/~shaulm/papers/pdf/Markovitch-Sella-coin1996.pdf pdf]
* [[David Carmel]], [[Shaul Markovitch]] ('''1993'''). ''Learning Models of Opponent's Strategy in Game Playing''. [[AAAI]] Proceedings, [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.6488 CiteSeerX]
* [[Dan Geiger]], [[Azaria Paz]], [[Judea Pearl]] ('''1993'''). ''Learning simple causal structures''. [http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291098-111X International Journal of Intelligent Systems], 8, pp. 231-247.
* [[Sebastian Thrun]], [[Tom Mitchell]] ('''1993'''). ''Integrating Inductive Neural Network Learning and Explanation-Based Learning''. [[Conferences#IJCAI1993|IJCAI 1993]], [http://robots.stanford.edu/papers/thrun.EBNN_ijcai93.ps.gz zipped ps]
* [[Alois Heinz]], [[Christoph Hense]] ('''1993'''). ''[http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.872 Bootstrap learning of α-β-evaluation functions]''. [http://dblp.uni-trier.de/db/conf/icci/icci1993.html#HeinzH93 ICCI 1993], [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.872&rep=rep1&type=pdf pdf]
'''1994'''
* [[Eduardo F. Morales]] ('''1994'''). ''Learning Patterns for Playing Strategies''. [[ICGA Journal#17_1|ICCA Journal, Vol. 17, No. 1]]
* [[Fernand Gobet]], [[Peter Jansen]] ('''1994'''). ''Towards a chess program based on a model of human memory.'' [[Advances in Computer Chess 7]] » [[CHUMP]]
* [[Michael Bain]] ('''1994'''). ''Learning Logical Exceptions in Chess''. Ph.D. thesis, [https://en.wikipedia.org/wiki/University_of_Strathclyde University of Strathclyde], [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.729 CitySeerX]
* [[Michael Bain]], [[Stephen Muggleton]] ('''1994'''). ''Learning Optimal Chess Strategies''. Machine Intelligence 13 (eds. K. Furukawa and [[Donald Michie]]), pp. 291-309. Oxford University Press, Oxford, UK. ISBN 0198538502.
* [[Ryszard Michalski]], [[George Tecuci]] ('''1994'''). ''Machine Learning: A Multistrategy Approach, Volume IV''. Morgan Kaufmann, ISBN 1-55860-251-8. [http://books.google.com/books?id=sQJ1PMEOOY0C&dq=isbn%3A0935382054&hl=de&source=gbs_book_other_versions google books]
* [[Gerald Tesauro]] ('''1994'''). ''TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play''. [http://www.informatik.uni-trier.de/~ley/db/journals/neco/neco6.html#Tesauro94 Neural Computation Vol. 6, No. 2]
* [[Alberto Maria Segre]], [[Charles Elkan]] ('''1994'''). ''A High-Performance Explanation-Based Learning Algorithm''. [https://en.wikipedia.org/wiki/Artificial_Intelligence_%28journal%29 Artificial Intelligence], Vol. 68, Nos. 1-2
* [[David E. Moriarty]], [[Risto Miikkulainen]] ('''1994'''). ''Evolving Neural Networks to focus Minimax Search''. [[AAAI|AAAI-94]], [http://www.cs.utexas.edu/~ai-lab/pubs/moriarty.focus.pdf pdf]
* [[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1994'''). ''[http://nic.schraudolph.org/bib2html/b2hd-SchDaySej94.html Temporal Difference Learning of Position Evaluation in the Game of Go]''. [http://papers.nips.cc/book/advances-in-neural-information-processing-systems-6-1993 Advances in Neural Information Processing Systems 6]
==1995 ...==
* [[Gerhard Mehlsam]], [[Hermann Kaindl]], [[Wilhelm Barth]] ('''1995'''). ''Feature Construction during Tree Learning''. [http://137.226.34.227/dblp/db/conf/gosler/gosler1995.html GOSLER Final Report] 1995: 391-403
* [[Chris McConnell]] ('''1995'''). ''Tuning Evaluation Functions for Search''. [http://www.cs.cmu.edu/afs/cs.cmu.edu/user/ccm/www/papers/ml.ps ps] or [http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=9B2A0CCA8B1AFB594A879799D974111A?doi=10.1.1.53.9742&rep=rep1&type=pdf pdf] from [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.9742 CiteSeerX]
* [[David Heckerman]], [[Dan Geiger]], [[Max Chickering]] ('''1995'''). ''Learning Bayesian Networks: The Combination of Knowledge and Statistical Data''. [https://en.wikipedia.org/wiki/Machine_Learning_%28journal%29 Machine Learning], Vol. 20, [http://research.microsoft.com/en-us/um/people/dmax/publications/ml95.pdf pdf]
* [[Tristan Cazenave]] ('''1995'''). ''Learning and Problem Solving in Gogol, a Go playing program''. [http://www.lamsade.dauphine.fr/~cazenave/papers/cazenave95learning.pdf pdf]
* [[Gerald Tesauro]] ('''1995'''). ''Temporal Difference Learning and TD-Gammon''. [[ACM#Communications|Communications of the ACM]] Vol. 38, No. 3
* [[Sebastian Thrun]] ('''1995'''). ''[http://robots.stanford.edu/papers/thrun.nips7.neuro-chess.html Learning to Play the Game of Chess]''. in [[Gerald Tesauro]], [https://en.wikipedia.org/wiki/David_S._Touretzky David S. Touretzky], [http://mitpress.mit.edu/authors/todd-k-leen Todd K. Leen] (eds.) Advances in Neural Information Processing Systems 7, [https://en.wikipedia.org/wiki/MIT_Press MIT Press]
* [[Marco Wiering]] ('''1995'''). ''[https://scholar.google.com/citations?view_op=view_citation&hl=en&user=xVas0I8AAAAJ&cstart=20&citation_for_view=xVas0I8AAAAJ:roLk4NBRz8UC TD Learning of Game Evaluation Functions with Hierarchical Neural Architectures]''. Master's thesis, [https://en.wikipedia.org/wiki/University_of_Amsterdam University of Amsterdam], [http://webber.physik.uni-freiburg.de/~hon/vorlss02/Literatur/reinforcement/GameEvaluationWithNeuronal.pdf pdf]
* [[Mathematician#MAArbib|Michael A. Arbib]] (ed.) ('''1995, 2002'''). ''[http://mitpress.mit.edu/books/handbook-brain-theory-and-neural-networks The Handbook of Brain Theory and Neural Networks]''. [https://en.wikipedia.org/wiki/MIT_Press The MIT Press]
* [[Nicol N. Schraudolph]] ('''1995'''). ''[http://nic.schraudolph.org/bib2html/b2hd-Schraudolph95 Optimization of Entropy with Neural Networks]''. Ph.D. thesis, [https://en.wikipedia.org/wiki/University_of_California,_San_Diego University of California, San Diego]
* [[Robert W. Howard]] ('''1995'''). ''Learning and Memory: Major Ideas, Principles, Issues and Applications''. Praeger, [http://www.amazon.com/Learning-Memory-Principles-Issues-Applications/dp/027594641X/ref=la_B001HPC7VM_1_1?ie=UTF8&qid=1352388827 amazon.com]
'''1996'''
* [http://dblp.uni-trier.de/pers/hd/b/Baird_III:Leemon_C= Leemon C. Baird III], [http://dblp.uni-trier.de/pers/hd/h/Harmon:Mance_E= Mance E. Harmon], [[A. Harry Klopf]] ('''1996'''). ''Reinforcement Learning: An Alternative Approach to Machine Intelligence''. [http://www.leemon.com/papers/1996bhk.pdf pdf]
* [[Sebastian Thrun]] ('''1996'''). ''[http://robots.stanford.edu/papers/thrun.book.html Explanation-Based Neural Network Learning: A Lifelong Learning Approach]''. [https://en.wikipedia.org/wiki/Wolters_Kluwer Kluwer Academic Publishers]
* [[Mathematician#LPKaelbling|Leslie Pack Kaelbling]], [[Michael L. Littman]], [[Mathematician#AWMoore|Andrew W. Moore]] ('''1996'''). ''[http://www.cs.washington.edu/research/jair/volume4/kaelbling96a-html/rl-survey.html Reinforcement Learning: A Survey]''. [http://www.jair.org/vol/vol4.html JAIR Vol. 4], [http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a.pdf pdf]
* [[Eduardo F. Morales]] ('''1996'''). ''Learning Playing Strategies in Chess''. [http://www.wiley.com/bw/journal.asp?ref=0824-7935 Computational Intelligence], Vol. 12, No. 1, [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.7592 CiteSeerX]
* [[Wee Sun Lee]] ('''1996'''). ''Agnostic Learning and Single Hidden Layer Neural Networks.'' Ph.D. thesis, [[Australian National University]], [http://www.comp.nus.edu.sg/~leews/publications/thesis.ps ps]
* [[Johannes Fürnkranz]] ('''1996'''). ''Machine Learning in Computer Chess: The Next Generation.'' [[ICGA Journal#19_3|ICCA Journal, Vol. 19, No. 3]], [http://www.ofai.at/cgi-bin/get-tr?download=1&paper=oefai-tr-96-11.ps.gz zipped ps]
* [[Adriaan de Groot]], [[Fernand Gobet]] ('''1996'''). ''[http://people.brunel.ac.uk/%7Ehsstffg/abstracts/deGroot_abstract.html Perception and memory in chess]. Heuristics of the professional eye.'' Assen: Van Gorcum, The Netherlands. ISBN 90-232-2949-5. Chapter 9; A discussion: Two authors, two different views? [http://people.brunel.ac.uk/%7Ehsstffg/preprints/DeGroot_Gobet_Chapter_9.doc word]
* [[Stuart Russell]] ('''1996'''). ''Machine Learning.'' Chapter 4 of M. A. Boden (Ed.), Artificial Intelligence, Academic Press. Part of the Handbook of Perception and Cognition, [http://www.cs.berkeley.edu/~russell/papers/hpc-ml.ps ps]
* [[Barney Pell]], [[Susan L. Epstein]], [[Robert Levinson]] ('''1996'''). ''Introduction to the special issue on games: Structure and Learning''. [http://dblp.uni-trier.de/db/journals/ci/ci12.html#PellEL96 Computational Intelligence, Vol. 12], No. 1, [http://www.barneypell.com/papers/intro-cij.pdf pdf]
* [[Robert Levinson]] ('''1996'''). ''[http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8640.1996.tb00257.x/abstract General Game-Playing and Reinforcement Learning]''. [http://dblp.uni-trier.de/db/journals/ci/ci12.html#PellEL96 Computational Intelligence, Vol. 12], No. 1
* [[Tristan Cazenave]] ('''1996'''). ''Learning to forecast by explaining the consequences of actions''. [http://www.lamsade.dauphine.fr/~cazenave/papers/malfo96.pdf pdf]
* [[Tristan Cazenave]] ('''1996'''). ''Self fuzzy learning''. [http://www.lamsade.dauphine.fr/~cazenave/papers/fuzzy.pdf pdf]
* [[Yoav Freund]], [[Robert Schapire]] ('''1996'''). ''Game Theory, On-line Prediction and Boosting''. [http://dblp.uni-trier.de/db/conf/colt/colt1996.html#FreundS96 COLT 1996], [http://www.cs.princeton.edu/~schapire/papers/FreundSc96b.pdf pdf]
'''1997'''
* [[Yoav Freund]], [[Robert Schapire]] ('''1997'''). ''A decision-theoretic generalization of on-line learning and an application to boosting''. [https://en.wikipedia.org/wiki/Journal_of_Computer_and_System_Sciences Journal of Computer and System Sciences], Vol. 55, No. 1, [http://cseweb.ucsd.edu/~yfreund/papers/adaboost.pdf 1996 pdf] » [https://en.wikipedia.org/wiki/AdaBoost AdaBoost]
* [[Mathematician#SHochreiter|Sepp Hochreiter]], [[Jürgen Schmidhuber]] ('''1997'''). ''Long short-term memory''. [https://en.wikipedia.org/wiki/Neural_Computation_%28journal%29 Neural Computation], Vol. 9, No. 8, [http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf pdf] <ref>[https://en.wikipedia.org/wiki/Long_short_term_memory Long short term memory from Wikipedia]</ref>
* [[Eduardo F. Morales]] ('''1997'''). ''On Learning How to Play''. [[Advances in Computer Chess 8]], [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.8701 CiteSeerX]
* [[Don Beal]], [[Martin C. Smith]] ('''1997'''). ''Learning Piece Values Using Temporal Differences''. [[ICGA Journal#20_3|ICCA Journal, Vol. 20, No. 3]]
* [[Kieran Greer]], [[Piyush Ojha]], [[David A. Bell]] ('''1997'''). ''Learning Search Heuristics from Examples: A Study in Computer Chess'', Seventh Conference of the Spanish Association for Artificial Intelligence, CAEPIA’97, November, pp. 695-704.
* [[Nir Friedman]], [[Moises Goldszmidt]], [[David Heckerman]], [[Stuart Russell]] ('''1997'''). ''Where is the Impact of Bayesian Networks in Learning?'' In Proc. Fifteenth International Joint Conference on Artificial Intelligence, Nagoya, Japan, [http://www.cs.berkeley.edu/~russell/papers/ijcai97-challenge.ps ps]
* [[Ronald Parr]], [[Stuart Russell]] ('''1997'''). ''Reinforcement Learning with Hierarchies of Machines.'' In Advances in Neural Information Processing Systems 10, MIT Press, [http://www.cs.berkeley.edu/~russell/papers/nips97-ham.ps.gz zipped ps]
* [[Tristan Cazenave]] ('''1997'''). ''Gogol (an Analytical Learning Program)''. [http://www.ijcai.org/past/ijcai-97/ IJCAI'97], [http://www.lamsade.dauphine.fr/~cazenave/papers/fost97.pdf pdf]
* [[Tom Mitchell]] ('''1997'''). ''[http://www.cs.cmu.edu/%7Etom/mlbook.html Machine Learning]''. [https://en.wikipedia.org/wiki/McGraw-Hill McGraw Hill]
* [[Michèle Sebag]] ('''1997'''). ''Stochastic Heuristics for Machine Learning & Machine Learning for Stochastic Optimization''. Habilitation, [https://en.wikipedia.org/wiki/Paris-Sud_11_University Paris-Sud 11 University]
* [[William Uther]], [[Manuela Veloso|Manuela M. Veloso]] ('''1997'''). ''Adversarial Reinforcement Learning''. [[Carnegie Mellon University]], [http://www.cse.unsw.edu.au/~willu/w/papers/Uther97a.ps ps]
* [[William Uther]], [[Manuela Veloso|Manuela M. Veloso]] ('''1997'''). ''Generalizing Adversarial Reinforcement Learning''. [[Carnegie Mellon University]], [http://www.cse.unsw.edu.au/~willu/w/papers/Uther97b.ps ps]
* [[Marco Wiering]], [[Jürgen Schmidhuber]] ('''1997'''). ''[https://scholar.google.com/citations?view_op=view_citation&hl=en&user=xVas0I8AAAAJ&citation_for_view=xVas0I8AAAAJ:u5HHmVD_uO8C HQ-learning]''. [https://en.wikipedia.org/wiki/Adaptive_Behavior_%28journal%29 Adaptive Behavior], Vol. 6, No 2
'''1998'''
* [[Jonathan Baxter]], [[Andrew Tridgell]], [[Lex Weaver]] ('''1998'''). ''Knightcap: A chess program that learns by combining td(λ) with game-tree search'', Proceedings of the 15th International Conference on Machine Learning, [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.54.8263&rep=rep1&type=pdf pdf] via [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.8263 citeseerX]
* [[Jonathan Baxter]], [[Andrew Tridgell]], [[Lex Weaver]] ('''1998'''). ''TDLeaf(lambda): Combining Temporal Difference Learning with Game-Tree Search''. [https://www.chatbots.org/journal/australian_journal_of_intelligent_information_processing_systems/ Australian Journal of Intelligent Information Processing Systems], Vol. 5 No. 1, [http://arxiv.org/abs/cs/9901001 arXiv:cs/9901001]
* [[Jonathan Baxter]], [[Andrew Tridgell]], [[Lex Weaver]] ('''1998'''). ''Experiments in Parameter Learning Using Temporal Differences''. [[ICGA Journal#21_2|ICCA Journal, Volume 21 No. 2]], [http://cs.anu.edu.au/%7ELex.Weaver/pub_sem/publications/ICCA-98_equiv.pdf pdf]
* [[Lev Finkelstein]], [[Shaul Markovitch]] ('''1998'''). ''[http://www.cs.technion.ac.il/%7Eshaulm/papers/abstracts/Finkelstein-1998-LPC.html Learning to Play Chess Selectively by Acquiring Move Patterns.]'' [[ICGA Journal#21_2|ICCA Journal, Vol. 21, No. 2]], [http://www.cs.technion.ac.il/%7Eshaulm/papers/pdf/Finkelstein-Markovitch-icca1998.pdf pdf]
* [[Csaba Szepesvári]] ('''1998'''). ''Reinforcement Learning: Theory and Practice''. Proceedings of the 2nd Slovak Conference on Artificial Neural Networks, [http://www.sztaki.hu/%7Eszcsaba/papers/scann98.ps.gz zipped ps]
* [[Richard Sutton]], [[Andrew Barto]] ('''1998'''). ''[https://mitpress.mit.edu/books/reinforcement-learning Reinforcement Learning: An Introduction]''. [https://en.wikipedia.org/wiki/MIT_Press MIT Press]
* [[Ryszard Michalski]], [[Ivan Bratko]], [[Miroslav Kubat]] (eds.) ('''1998'''). ''[http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0471971995.html Machine Learning and Data Mining: Methods and Applications]''. [https://en.wikipedia.org/wiki/John_Wiley_%26_Sons John Wiley & Sons]
: [[Miroslav Kubat]], [[Ivan Bratko]], [[Ryszard Michalski]] ('''1998'''). ''A Review of Machine Learning Methods''. [http://lacam.di.uniba.it/people/courses/IA/IA1213/IA/letture/MLReview.pdf pdf]
* [[Nobusuke Sasaki]], [[Yasuji Sawada]], [[Jin Yoshimura]] ('''1998'''). ''[http://www.mendeley.com/research/a-neural-network-program-of-tsumego/ A Neural Network Program of Tsume-Go]''. [[CG 1998]] <ref>[https://en.wikipedia.org/wiki/Tsumego Tsumego from Wikipedia]</ref>
* [[Tristan Cazenave]] ('''1998'''). ''Machine Introspection for Machine Learning''. Tucson 1998, [http://www.lamsade.dauphine.fr/~cazenave/papers/tucson1998.pdf pdf]
* [[Tristan Cazenave]] ('''1998'''). ''Integration of Different Reasoning Modes in a Go Playing and Learning System''. [http://www.lamsade.dauphine.fr/~cazenave/papers/multimodal98.pdf pdf]
* [[Tristan Cazenave]] ('''1998'''). ''Learning with Fuzzy Definitions of Goals''. [http://www.lamsade.dauphine.fr/~cazenave/papers/lpsc.pdf pdf]
* [[Ryszard Michalski]] ('''1998'''). ''Learnable Evolution: Combining Symbolic and Evolutionary Learning''. Proceedings of the Fourth International Workshop on Multistrategy Learning (MSL'98)
* [[Krzysztof Krawiec]], [http://www.informatik.uni-trier.de/~ley/pers/hd/s/Slowinski:Roman.html Roman Slowinski], [http://www.informatik.uni-trier.de/~ley/pers/hd/s/Szczesniak:Irmina.html Irmina Szczesniak] ('''1998'''). ''[http://link.springer.com/chapter/10.1007%2F3-540-69115-4_60 Pedagogical Method for Extraction of Symbolic Knowledge from Neural Networks]''. [http://link.springer.com/book/10.1007%2F3-540-69115-4 Rough Sets and Current Trends in Computing 1998]
* [[Marco Wiering]], [[Jürgen Schmidhuber]] ('''1998'''). ''[https://scholar.google.com/citations?view_op=view_citation&hl=en&user=xVas0I8AAAAJ&citation_for_view=xVas0I8AAAAJ:2osOgNQ5qMEC Fast online Q (λ)]''. [https://en.wikipedia.org/wiki/Machine_Learning_%28journal%29 Machine Learning], Vol. 33, No. 1
'''1999'''
* [[Robert Hyatt]] ('''1999'''). ''[http://www.craftychess.com/hyatt/learning.html Book Learning - a Methodology to Tune an Opening Book Automatically]''. [[ICGA Journal#22_1|ICCA Journal, Vol. 22, No. 1]]
* [[Kieran Greer]], [[Piyush Ojha]], [[David A. Bell]] ('''1999'''). ''A Pattern-Oriented Approach to Move Ordering: the Chessmaps Heuristic''. [[ICGA Journal#22_1|ICCA Journal, Vol. 22, No. 1]]
* [[Michael Buro]] ('''1999'''). ''Toward Opening Book Learning.'' [[ICGA Journal#22_2|ICCA Journal, Vol. 22, No. 2]], [http://www.cs.ualberta.ca/%7Emburo/ps/book.pdf pdf]
* [[Don Beal]], [[Martin C. Smith]] ('''1999'''). ''Learning Piece-Square Values using Temporal Differences.'' [[ICGA Journal#22_4|ICCA Journal, Vol. 22, No. 4]]
* [[David Heckerman]] ('''1999'''). ''A tutorial on learning with Bayesian networks''. [http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=F04616607A620324B33D40A8ABB702CB?doi=10.1.1.15.4522&rep=rep1&type=pdf pdf] from [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.4522 CiteSeerX]
* [http://www2.lifl.fr/%7Edecomite/ F. De Comité], [http://www.lif.univ-mrs.fr/%7Efdenis/ F. Denis], [http://www.grappa.univ-lille3.fr/%7Egilleron/ R. Gilleron] et [[Fabien Letouzey]] ('''1999'''). ''Positive and Unlabeled Examples help Learning'', The 10th International Conference on Algorithmic Learning Theory, [http://www.cmi.univ-mrs.fr/%7Efdenis/alt99.ps ps]
* [http://www.ilsp.gr/homepages/papavasiliou_eng.html Vassilis Papavassiliou], [[Stuart Russell]] ('''1999'''). ''Convergence of reinforcement learning with general function approximators.'' In Proc. IJCAI-99, Stockholm, [http://www.cs.berkeley.edu/~russell/papers/ijcai99-bridge.ps ps]
* [[Philip G. K. Reiser]], [[Patricia J. Riddle]] ('''1999'''). ''[http://link.springer.com/chapter/10.1007%2F3-540-48873-1_19 Evolving Logic Programs to Classify Chess-Endgame Positions]''. [http://link.springer.com/book/10.1007%2F3-540-48873-1 Simulated Evolution and Learning], [https://en.wikipedia.org/wiki/Canberra Canberra], Australia. [http://www.springer.com/series/1244 Lecture Notes in Artificial Intelligence], No. 1585, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer], [http://stancomb.co.uk/Papers/seal98.pdf pdf] » [[Endgame]]
* [[Marco Wiering]] ('''1999'''). ''[https://scholar.google.com/citations?view_op=view_citation&hl=en&user=xVas0I8AAAAJ&pagesize=100&citation_for_view=xVas0I8AAAAJ:9yKSN-GCB0IC Explorations in Efficient Reinforcement Learning]''. Ph.D. thesis, [https://en.wikipedia.org/wiki/University_of_Amsterdam University of Amsterdam], advisors [[Mathematician#FGroen|Frans Groen]] and [[Jürgen Schmidhuber]]
* [[Mathematician#GEHinton|Geoffrey E. Hinton]], [[Terrence J. Sejnowski]] (eds.) ('''1999'''). ''[https://mitpress.mit.edu/books/unsupervised-learning Unsupervised Learning: Foundations of Neural Computation]''. [https://en.wikipedia.org/wiki/MIT_Press MIT Press]
==2000 ...==
* [[Miroslav Kubat]], [[Jan Žižka]] ('''2000'''). ''[http://link.springer.com/chapter/10.1007%2F3-540-45049-1_52 Learning Middle Game Patterns in Chess: A Case Study]''. [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 1821, [https://de.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [[Mathematician#VNVapnik|Vladimir Vapnik]] ('''2000'''). ''[http://www.springer.com/us/book/9780387987804 The nature of statistical learning theory]''. [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [[Sebastian Thrun]], [[Michael L. Littman]] ('''2000'''). ''A Review of Reinforcement Learning''. [http://www.informatik.uni-trier.de/~ley/db/journals/aim/aim21.html#ThrunL00 AI Magazine, Vol. 21], No. 1
* [[Johannes Fürnkranz]] ('''2000'''). ''Machine Learning in Games: A Survey''. [https://en.wikipedia.org/wiki/Austrian_Research_Institute_for_Artificial_Intelligence Austrian Research Institute for Artificial Intelligence], OEFAI-TR-2000-3, [http://www.ofai.at/cgi-bin/get-tr?download=1&paper=oefai-tr-2000-31.pdf pdf]
* [[Johannes Fürnkranz]], [[Bernhard Pfahringer]], [[Hermann Kaindl]], [[Stefan Kramer]] ('''2000'''). ''[https://scholar.google.com/citations?view_op=view_citation&hl=en&user=0QXx46sAAAAJ&cstart=20&pagesize=80&citation_for_view=0QXx46sAAAAJ:k_IJM867U9cC Learning to Use Operational Advice]''. ECAI-00, [http://www.ke.informatik.tu-darmstadt.de/%7Ejuffi/publications/ecai-00.pdf pdf]
* [[Jack van Rijswijck]] ('''2000'''). ''[http://link.springer.com/chapter/10.1007/3-540-45579-5_8 Learning from Perfection: A Data Mining Approach to Evaluation Function Learning in Awari]''. [[CG 2000]], [http://sites.google.com/site/javhar1/LearningFromPerfection.pdf pdf]
* [[Robert Levinson]], [[Ryan Weber]] ('''2000'''). ''[http://link.springer.com/chapter/10.1007/3-540-45579-5_9 Chess Neighborhoods, Function Combination, and Reinforcement Learning]''. [[CG 2000]]
* [[Jan Ramon]], [[Tom Francis]], [[Hendrik Blockeel]] ('''2000'''). ''[http://link.springer.com/chapter/10.1007/3-540-45579-5_10 Learning a Go Heuristic with Tilde]''. [[CG 2000]]
* [[Levente Kocsis]], [[Jos Uiterwijk]], [[Jaap van den Herik]] ('''2000'''). ''[http://link.springer.com/chapter/10.1007/3-540-45579-5_11 Learning Time Allocation using Neural Networks]''. [[CG 2000]], [http://zaphod.aml.sztaki.hu/papers/kocsis-CG00.ps postscript]
* [[Michael Buro]] ('''2000'''). ''Toward Opening Book Learning.'' Games in AI Research (eds. [[Jaap van den Herik]] and [[Hiroyuki Iida]]), pp. 47-54. [[Maastricht University|Universiteit Maastricht]], Maastricht, The Netherlands. ISBN 90-621-6416-1.
* [[Fabien Letouzey]], [http://www.lif.univ-mrs.fr/%7Efdenis/ François Denis], [http://www.grappa.univ-lille3.fr/%7Egilleron/ Rémi Gilleron] ('''2000'''). ''Learning from Positive and Unlabeled Examples''. ALT 2000: 71-85, [http://www.cmi.univ-mrs.fr/%7Efdenis/alt00a.ps ps]
* [[Andrew Ng]], [[Stuart Russell]] ('''2000'''). ''Algorithms for inverse reinforcement learning.'' In Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, California: Morgan Kaufmann, [http://www.cs.berkeley.edu/~russell/papers/ml00-irl.pdf pdf]
* [http://www.cs.ou.edu/~hougen/ Dean F. Hougen], [http://www-users.cs.umn.edu/~gini/ Maria Gini], [[James R. Slagle]] ('''2000'''). ''[http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.2633 An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control]''. ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
* [[Ryszard Michalski]] ('''2000'''). ''LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning''. [https://en.wikipedia.org/wiki/Machine_Learning_%28journal%29 Machine Learning], Vol. 38 <ref>[https://en.wikipedia.org/wiki/Learnable_Evolution_Model Learnable Evolution Model from Wikipedia]</ref>
* [[Jonathan Baxter]], [[Andrew Tridgell]], [[Lex Weaver]] ('''2000'''). ''Learning to Play Chess Using Temporal Differences''. [http://www.dblp.org/db/journals/ml/ml40.html#BaxterTW00 Machine Learning, Vol 40, No. 3], [http://www.cs.princeton.edu/courses/archive/fall06/cos402/papers/chess-RL.pdf pdf]
* [[Michael Bain]], [[Stephen Muggleton]], [[Ashwin Srinivasan]] ('''2000'''). ''Generalising Closed World Specialisation: A Chess End Game Application''. [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.24.3499 CitySeerX]
'''2001'''
* [[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''2001'''). ''[http://nic.schraudolph.org/bib2html/b2hd-SchDaySej01.html Learning to Evaluate Go Positions via Temporal Difference Methods]''. in [[Norio Baba]], [[Lakhmi C. Jain]] (eds.) ('''2001'''). ''[http://jasss.soc.surrey.ac.uk/7/1/reviews/takama.html Computational Intelligence in Games, Studies in Fuzziness and Soft Computing]''. [http://www.springer.com/economics?SGWID=1-165-6-73481-0 Physica-Verlag], revised version of [[Nicol N. Schraudolph#1994|1994 paper]]
* [[Jonathan Schaeffer]], [[Markian Hlynka]], [[Vili Jussila]] ('''2001'''). ''Temporal Difference Learning Applied to a High-Performance Game-Playing Program''. [http://www.informatik.uni-trier.de/~ley/db/conf/ijcai/ijcai2001.html#SchaefferHJ01 IJCAI 2001]
* [[Michael Bowling]], [[Manuela Veloso|Manuela M. Veloso]] ('''2001'''). ''Rational and Convergent Learning in Stochastic Games''. [http://www.informatik.uni-trier.de/~ley/db/conf/ijcai/ijcai2001.html#BowlingV01 IJCAI 2001]
* [[Levente Kocsis]], [[Jos Uiterwijk]], [[Jaap van den Herik]] ('''2001'''). ''Move Ordering using Neural Networks'', IEA/AIE 2001, LNCS 2070, 45-50 [http://zaphod.aml.sztaki.hu/papers/kocsis-IEA01.ps ps]
* [[Marty Hirsch]] ('''2001'''). ''Machine Learning in MChess Professional''. [[Advances in Computer Games 9]]
* [[Yngvi Björnsson]], [[Tony Marsland]] ('''2001'''). ''Learning Search Control in Adversary Games''. [[Advances in Computer Games 9]], pp. 157-174. [http://www.ru.is/faculty/yngvi/pdf/BjornssonM01b.pdf pdf]
* [[Robert Levinson]], [[Ryan Weber]] ('''2001'''). ''Chess Neighborhoods, Function Combinations and Reinforcements Learning''. In Computers and Games (eds. [[Tony Marsland]] and I. Frank). [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science],. Springer,. [http://users.soe.ucsc.edu/~levinson/Papers/CNFCRL.pdf pdf]
* [[Jean Hayes Michie]] ('''2001'''). ''[http://www.aaai.org/ojs/index.php/aimagazine/article/view/1599/0 Machine Learning and Light Relief: A Review of Truth from Trash]''. [http://www.informatik.uni-trier.de/~ley/db/journals/aim/aim22.html#Michie01 AI Magazine Vol. 22 No. 4], [http://www.aaai.org/ojs/index.php/aimagazine/article/download/1599/1498 pdf]
* [[Pieter Spronck]], [[Ida Sprinkhuizen-Kuyper]], [[Eric Postma]] ('''2001'''). ''Infused Evolutionary Learning''. Proceedings of the Eleventh Belgian-Dutch Conference on Machine Learning, [http://www.cnts.ua.ac.be/benelearn2001/proceedings/bene01-spronck.pdf pdf], [http://ticc.uvt.nl/~pspronck/pubs/InfusedEvolutionaryLearning.pdf pdf]
* [[Charles Elkan]] ('''2001'''). ''The Foundations of Cost-Sensitive Learning''. [[Conferences#IJCAI|IJCAI 2001]]
* [[Alex B. Meijer]], [[Henk Koppelaar]] ('''2001'''). ''[http://www.kbs.twi.tudelft.nl/Publications/Conference/2001/2001-MeijerKoppelaar-GAMEON01.html A learning architecture for the game of Go]''. [https://www.informs.org/Attend-a-Conference/Conference-Calendar/Game-On-2001 Game-On 2001]
* [[Johannes Fürnkranz]], [[Miroslav Kubat]] ('''2001'''). ''[https://www.novapublishers.com/catalog/product_info.php?products_id=720 Machines that Learn to Play Games]''. Advances in Computation: Theory and Practice, Vol. 8,. [https://en.wikipedia.org/wiki/Nova_Publishers NOVA Science Publishers]
'''2002'''
* [[Yngvi Björnsson]], [[Tony Marsland]] ('''2002'''). ''Learning Control of Search Extensions''. Proceedings of the 6th Joint Conference on Information Sciences (JCIS 2002), pp. 446-449. [http://www.ru.is/faculty/yngvi/pdf/BjornssonM02.pdf pdf]
* [[Michael Buro]] ('''2002'''). ''Improving Mini-max Search by Supervised Learning.'' Artificial Intelligence, Vol. 134, No. 1, pp. 85-99. ISSN 0004-3702. [http://www.cs.ualberta.ca/%7Emburo/ps/logaij.pdf pdf]
* [[Levente Kocsis]], [[Jos Uiterwijk]], [[Eric Postma]], [[Jaap van den Herik]] ('''2002'''). ''The Neural MoveMap Heuristic in Chess''. [[CG 2002]], [http://zaphod.aml.sztaki.hu/papers/kocsis-CG02.ps ps]
* [[Erik van der Werf]], [[Jos Uiterwijk]], [[Eric Postma]], [[Jaap van den Herik]] ('''2002'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-540-40031-8_26 Local Move Prediction in Go]''. [[CG 2002]]
* [[Ari Shapiro]], [[Gil Fuchs]], [[Robert Levinson]] ('''2002'''). ''[http://www.arishapiro.com/researchportfolio/Learning%20Game%20Strategy/index.htm Learning a Game Strategy Using Pattern-Weights and Self-play]''. [[CG 2002]], [http://www.arishapiro.com//ShapiroA_CG2002.pdf pdf]
* [[Mark Winands]], [[Levente Kocsis]], [[Jos Uiterwijk]], [[Jaap van den Herik]] ('''2002'''). ''Temporal difference learning and the Neural MoveMap heuristic in the game of Lines of Action''. In Mehdi, Q,., Gouch, N., and Cavazza, M., editors, GAME-ON 2002 3rd International Conference on Intelligent Games and Simulation, pages 99-103. SCS Europe Bvba. [http://zaphod.aml.sztaki.hu/papers/winands-GAMEON02.pdf pdf]
* [[Roman Grekovs]] ('''2002'''). ''Methods of Fuzzy Pattern Recognition'' [https://en.wikipedia.org/wiki/Riga_Technical_University Riga Technical University], [http://www.cs.rtu.lv/dssg/download/publications/2002/Grekov-RTU-2002.ps.gz%7CMethods ps], covers Fuzzy Kora algorithm
* [[Pieter Spronck]], [[Ida Sprinkhuizen-Kuyper]], [[Eric Postma]] ('''2003'''). ''Improved opponent intelligence trough offline learning''. [http://www.informatik.uni-trier.de/~ley/db/journals/ijigs/ijigs2.html#SpronckSP03 International Journal of Intelligent Games & Simulation, Vol. 2]
* [[Krzysztof Krawiec]] ('''2002'''). ''[http://link.springer.com/article/10.1023/A:1020984725014 Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks]''. [http://www.informatik.uni-trier.de/~ley/db/journals/gpem/gpem3.html#Krawiec02 Genetic Programming and Evolvable Machines, Vol. 3], No. 4
* [[Peter Auer]], [[Nicolò Cesa-Bianchi]], [[Paul Fischer]] ('''2002'''). ''[http://link.springer.com/article/10.1023%2FA%3A1013689704352 Finite-time Analysis of the Multiarmed Bandit Problem]''. [https://en.wikipedia.org/wiki/Machine_Learning_%28journal%29 Machine Learning], Vol. 47, No. 2, [http://homes.di.unimi.it/~cesabian/Pubblicazioni/ml-02.pdf pdf]
* [[Paul E. Utgoff]], [[David J. Stracuzzi]] ('''2002'''). ''Many-Layered Learning''. [https://en.wikipedia.org/wiki/Neural_Computation_%28journal%29 Neural Computation], Vol. 14, No. 10, [http://people.cs.umass.edu/~utgoff/papers/neco-stl.pdf pdf]
'''2003'''
* [[Levente Kocsis]], [[Jaap van den Herik]], [[Jos Uiterwijk]] ('''2003'''). ''Two Learning Algorithms for Forward Pruning''. [[ICGA Journal#26_3|ICGA Journal, Vol 26, No. 3]], [http://zaphod.aml.sztaki.hu/papers/kocsis-ICGA03.ps ps]
* [[Levente Kocsis]] ('''2003''') ''[http://www.cs.unimaas.nl/%7Euiterwyk/KocsisPhD.htm Learning Search Decisions]'', '''PhD thesis''', [[Maastricht University|Universiteit Maastricht]] [http://zaphod.aml.sztaki.hu/papers/kocsis-thesis.ps ps]
* [[Marco Block-Berlitz]] ('''2003'''). ''Reinforcement Learning in der Schachprogrammierung''. Studienarbeit, Freie Universität Berlin, Dozent: [[Raúl Rojas|Prof. Dr. Raúl Rojas]], [http://page.mi.fu-berlin.de/block/Skripte/Reinforcement.pdf pdf] (German)
* [[Dave Gomboc]], [[Tony Marsland]], [[Michael Buro]] ('''2003'''). ''Evaluation Function Tuning via Ordinal Correlation''. [[Advances in Computer Games 10]], [http://www.top-5000.nl/ps/Dave%20Gomboc%20-%20Evaluation%20Tuning.pdf pdf]
* [[Stuart Russell]], [[Peter Norvig]] ('''2003'''). ''[http://aima.cs.berkeley.edu/ Artificial Intelligence: A Modern Approach]''. 2nd edition, [http://books.google.com/books?id=8jZBksh-bUMC&dq=isbn:0137903952&hl=en 3rd edition 2009]
* [[Judea Pearl]], [[Stuart Russell]] ('''2003'''). ''Bayesian Networks.'' In Michael A. Arbib, Ed., The Handbook of Brain Theory and Neural Networks, 2nd edition, MIT Press, [http://www.cs.berkeley.edu/~russell/papers/hbtnn-bn.pdf pdf]
* [http://www.inference.phy.cam.ac.uk/mackay/ David J.C. MacKay] ('''2003'''). ''[http://www.inference.phy.cam.ac.uk/mackay/itila/ Information Theory, Inference, and Learning Algorithms]''.
* [[Pedro Campos]], [[Thibault Langlois]] ('''2003'''). ''[http://ilk.uvt.nl/icga/journal/contents/content26-4.htm#ABALEARN Abalearn: a Program that Learns How to Play Abalone]''. [[ICGA Journal#26_4|ICGA Journal, Vol. 26, No. 4]]
* [[David Gleich]] ('''2003'''). ''Machine Learning in Computer Chess: Genetic Programming and KRK''. [https://en.wikipedia.org/wiki/Harvey_Mudd_College Harvey Mudd College], [http://www.cs.purdue.edu/homes/dgleich/publications/Gleich%202003%20-%20Machine%20Learning%20in%20Computer%20Chess.pdf pdf]
* [[Henk Mannen]] ('''2003'''). ''Learning to play chess using reinforcement learning with database games''. Master’s thesis, [http://students.uu.nl/en/hum/cognitive-artificial-intelligence Cognitive Artificial Intelligence], [https://en.wikipedia.org/wiki/Utrecht_University Utrecht University]
* [[Jan Žižka]], [[Michal Mádr]] ('''2003'''). ''[https://www.muni.cz/research/publications/490371 Learning Representative Patterns from Real Chess Positions: A Case Study]''. [http://dblp.uni-trier.de/db/conf/iicai/iicai2003.html#ZizkaM03 IICAI 2003]
'''2004'''
* [[Yngvi Björnsson]], Vignir Hafsteinsson, Ársæll Jóhannsson, Einar Jónsson ('''2004'''). ''Efficient Use of Reinforcement Learning in a Computer Game''. In Computer Games: Artificial Intellignece, Design and Education (CGAIDE'04), pp. 379–383, 2004. [http://www.ru.is/faculty/yngvi/pdf/BjornssonHJJ04.pdf pdf]
* [[Dave Gomboc]] ('''2004'''). ''Tuning Evaluation Functions by Maximizing Concordance'' Master of Science Thesis, [http://people.ict.usc.edu/%7Egomboc/publications/2004/M.Sc.-Thesis.online.pdf pdf]
* [[Adam Marczyk]] ('''2004'''). ''[http://www.talkorigins.org/faqs/genalg/genalg.html Genetic Algorithms and Evolutionary Computation]'' from the [https://en.wikipedia.org/wiki/TalkOrigins_Archive TalkOrigins Archive]
* [[Petr Aksenov]] ('''2004'''). ''[http://joypub.joensuu.fi/publications/masters_thesis/aksenov_genetic/index_en.html Genetic algorithms for optimising chess position scoring]'', Masters thesis, [ftp://cs.joensuu.fi/pub/Theses/2004_MSc_Aksenov_Petr.pdf pdf]
* [[Marek Strejczek]] ('''2004'''). ''Some aspects of chess programming'', [[Technical University of Łódź]] , Faculty of Electrical and Electronic Engineering, Department of Computer Science, [http://nesik.republika.pl/download//SomeAspectsOfChessProgramming.zip zipped pdf]
* [http://imranontech.com/ Imran Ghory] ('''2004'''). ''Reinforcement learning in board games''. CSTR-04-004, [http://www.cs.bris.ac.uk/ Department of Computer Science], [https://en.wikipedia.org/wiki/University_of_Bristol University of Bristol]. [http://www.cs.bris.ac.uk/Publications/Papers/2000100.pdf pdf] <ref>[http://www.cs.bris.ac.uk/Publications/pub_master.jsp?type=117 University of Bristol - Department of Computer Science - Technical Reports]</ref>
* [[Mathieu Autonès]], [[Aryel Beck]], [[Phillippe Camacho]], [[Nicolas Lassabe]], [[Hervé Luga]], [[François Scharffe]] ('''2004'''). ''[http://link.springer.com/chapter/10.1007/978-3-540-24650-3_1 Evaluation of Chess Position by Modular Neural network Generated by Genetic Algorithm]''. [http://www.informatik.uni-trier.de/~ley/db/conf/eurogp/eurogp2004.html#AutonesBCLLS04 EuroGP 2004]
* [[Jean-Yves Audibert]] ('''2004'''). ''PAC-Bayesian Statistical Learning Theory''. Ph.D. thesis, [https://en.wikipedia.org/wiki/Pierre_and_Marie_Curie_University Université Paris VI], [http://certis.enpc.fr/~audibert/Mes%20articles/PhDthesis.pdf pdf], [http://certis.enpc.fr/~audibert/Mes%20articles/audibert_PhD_defense.pdf slides as pdf]
* [[Eric Wiewiora]] ('''2004'''). ''Efficient Exploration for Reinforcement Learning''. MSc thesis, [http://cseweb.ucsd.edu/%7Eewiewior/04efficient.pdf pdf]
* [[David B. Fogel]], [[Timothy J. Hays]], [[Sarah L. Hahn]], [[James Quon]] ('''2004'''). ''[http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1360168 A Self-Learning Evolutionary Chess Program]''. [[IEEE#Proceedings|Proceedings of the IEEE]], Vol. 92 No. 12, pp. 1947-1954, [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.72.4267 CiteSeerX]
* [[Alejandro González Romero]], [http://www.lsi.upc.edu/~alquezar/ René Alquézar] ('''2004'''). ''[http://www.springerlink.com/content/rbbxnr865xyjbf4c/ Learning Through the KRKa2 Chess Ending]''. [http://www.informatik.uni-trier.de/~ley/db/conf/ciarp/ciarp2004.html CIARP 2004]
* [[Daniel Osman]], [[Jacek Mańdziuk]] ('''2004'''). ''Comparison of TDLeaf and TD learning in Game Playing Domain''. [http://www.informatik.uni-trier.de/~ley/db/conf/iconip/iconip2004.html#OsmanM04 11. ICONIP], [http://www.mini.pw.edu.pl/~mandziuk/PRACE/ICONIP04.pdf pdf]
* [[Albert Xin Jiang]] ('''2004'''). ''Multiagent Reinforcement Learning in Stochastic Games with Continuous Action Spaces''. [http://www.cs.ubc.ca/%7Ejiang/papers/continuous.pdf pdf]
* [[Henk Mannen]], [[Marco Wiering]] ('''2004'''). ''[http://scholar.google.com/citations?view_op=view_citation&hl=en&user=xVas0I8AAAAJ&cstart=20&pagesize=80&citation_for_view=xVas0I8AAAAJ:7PzlFSSx8tAC Learning to play chess using TD(λ)-learning with database games]''. [http://students.uu.nl/en/hum/cognitive-artificial-intelligence Cognitive Artificial Intelligence], [https://en.wikipedia.org/wiki/Utrecht_University Utrecht University], Benelearn’04
==2005 ...==
* [[Dave Gomboc]], [[Michael Buro]], [[Tony Marsland]] ('''2005'''). ''Tuning evaluation functions by maximizing concordance'' Theoretical Computer Science, Volume 349, Issue 2, pp. 202-229, [http://www.cs.ualberta.ca/%7Emburo/ps/tcs-learn.pdf pdf]
* [[David B. Fogel]], [[Timothy J. Hays]], [[Sarah L. Hahn]], [[James Quon]] ('''2005'''). ''Further Evolution of a Self-Learning Chess Program''. [[IEEE#CIG|IEEE Symposium on Computational Intelligence & Games]], [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.5288 CiteSeerX]
* [[Tristan Caulfield]], [[Mathematician#JJBryson|Joanna J. Bryson]] ('''2005'''). ''Chess by Imitation''. Department of Computer Science, [https://en.wikipedia.org/wiki/University_of_Bath University of Bath], [http://www.cs.bath.ac.uk/~cs1tjc/TCaisb05.pdf pdf] <ref>[https://en.wikipedia.org/wiki/Generalized_Hebbian_Algorithm Generalized Hebbian Algorithm from Wikipedia]</ref>
* [[Marco Wiering]], [http://dblp.uni-trier.de/pers/hd/p/Patist:Jan_Peter Jan Peter Patist], [[Henk Mannen]] ('''2005'''). ''Learning to Play Board Games using Temporal Difference Methods''. Technical Report, [https://en.wikipedia.org/wiki/Utrecht_University Utrecht University], UU-CS-2005-048, [http://www.ai.rug.nl/~mwiering/GROUP/ARTICLES/learning_games_TR.pdf pdf]
* [[David J. Stracuzzi]] ('''2005'''). ''Scalable learning in many layers''. [https://en.wikipedia.org/wiki/University_of_Massachusetts_Amherst University of Massachusetts Amherst], TR-05-02, [http://www.stracuzzi.info/david/manuscripts/tr-05-02.pdf pdf]
* [[Levente Kocsis]], [[Csaba Szepesvári]], [[Mark Winands]] ('''2005'''). ''[http://link.springer.com/chapter/10.1007/11922155_4 RSPSA: Enhanced Parameter Optimization in Games]''. [[Advances in Computer Games 11]], [http://www.sztaki.hu/~szcsaba/papers/rspsa_acg.pdf pdf]
* [[Christian Posthoff]], [[Michael Schlosser]] ('''2005'''). ''[http://link.springer.com/chapter/10.1007%2F3-540-60217-8_17 Optimal strategies — Learning from examples — Boolean equations]''. in [https://www.researchgate.net/profile/Klaus_Jantke Klaus P. Jantke], [https://www.fbi.h-da.de/organisation/personen/lange-steffen.html Steffen Lange] (eds.) ('''2005'''). [http://link.springer.com/book/10.1007/3-540-60217-8 Algorithmic Learning for Knowledge-Based Systems], [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science] 961, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
'''2006'''
* [[Levente Kocsis]], [[Csaba Szepesvári]] ('''2006'''). ''[http://link.springer.com/article/10.1007/s10994-006-6888-8 Universal Parameter Optimisation in Games Based on SPSA]''. [https://en.wikipedia.org/wiki/Machine_Learning_%28journal%29 Machine Learning], Special Issue on Machine Learning and Games, Vol. 63, No. 3
* [http://nemendur.ru.is/sverrirs01/ Sverrir Sigmundarson], [[Yngvi Björnsson]]. ('''2006''') ''Value Back-Propagation vs. Backtracking in Real-Time Search.'' In Proceedings of the National Conference on Artificial Intelligence ([[AAAI]]), Workshop on Learning For Search, pp. 136–141, AAAI Press, Boston, Massachusetts, USA, July 2006. [http://www.ru.is/faculty/yngvi/pdf/SigmundarsonB06b.pdf pdf]
* [[Sylvain Gelly]], [[Olivier Teytaud]], [[Nicolas Bredèche]], [[Marc Schoenauer]] ('''2006'''). ''[http://eprints.pascal-network.org/archive/00002724/ Universal Consistency and Bloat in GP]. Some theoretical considerations about Genetic Programming from a Statistical Learning Theory viewpoint.'' [http://eprints.pascal-network.org/archive/00002724/01/riabloat.pdf pdf]
* [[Sylvain Gelly]], [[Jérémie Mary]], [[Olivier Teytaud]] ('''2006'''). ''Learning for stochastic dynamic programming''. [http://www.lri.fr/%7Egelly/paper/lfordp.pdf pdf]
* [[Olivier Teytaud]], [[Sylvain Gelly]] ('''2006'''). ''General lower bounds for evolutionary algorithms.'' [http://www.lri.fr/%7Egelly/paper/lblong.pdf pdf]
* [[Makoto Miwa]], [[Daisaku Yokoyama]], [[Takashi Chikayama]] ('''2006'''). ''[http://www.springerlink.com/content/6180u7h3t312468u/ Automatic Construction of Static Evaluation Functions for Computer Game Players]''. ALT ’06
* [[Tom Mitchell]] ('''2006'''). ''The Discipline of Machine Learning''. CMU-ML-06-108, [[Carnegie Mellon University]], [http://www.cs.cmu.edu/%7Etom/pubs/MachineLearning.pdf pdf]
* [[Tom Mitchell]] ('''2006'''). ''Human and Machine Learning''. [[Carnegie Mellon University]], [http://www.cs.cmu.edu/%7Etom/pubs/HumanMachineLearning_11_2006_web.pdf slides as pdf]
* [[Jeff Rollason]] ('''2006'''). ''[http://www.aifactory.co.uk/newsletter/2005_04_stronger-by-learning.htm Playing Stronger by learning]''. [[AI Factory]], Winter 2006
* [[Simon Lucas]], [[Thomas Philip Runarsson]] ('''2006'''). ''[http://scholar.google.is/citations?view_op=view_citation&hl=en&user=4eWdc_sAAAAJ&citation_for_view=4eWdc_sAAAAJ:qjMakFHDy7sC Temporal Difference Learning versus Co-Evolution for Acquiring Othello Position Evaluation]''. [[IEEE#CIG|IEEE Symposium on Computational Intelligence and Games]] » [[Othello]]
* [[Nicolò Cesa-Bianchi]], [[Gábor Lugosi]] ('''2006'''). ''[http://homes.di.unimi.it/~cesabian/predbook/ Prediction, Learning, and Games]''. [https://en.wikipedia.org/wiki/Cambridge_University_Press Cambridge University Press]
* [[David J. Stracuzzi]] ('''2006'''). ''[http://scholarworks.umass.edu/dissertations/AAI3242366/ Scalable Knowledge Acquisition through Cumulative Learning and Memory Organization]''. Ph.D. thesis, [https://en.wikipedia.org/wiki/University_of_Massachusetts_Amherst University of Massachusetts Amherst], advisor [[Paul E. Utgoff]], [http://stracuzzi.info/david/manuscripts/dissertation06.pdf pdf]
* [[Michael Bowling]], [[Johannes Fürnkranz]], [[Thore Graepel]], [http://dblp.uni-trier.de/pers/hd/m/Musick:Ron Ron Musick] ('''2006'''). ''[https://link.springer.com/article/10.1007/s10994-006-8919-x Machine learning and Games]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 63, No. 3
'''2007'''
* [[Sylvain Gelly]], [[Olivier Teytaud]], [[Jérémie Mary]] ('''2007'''). ''Active learning in regression, with application to stochastic dynamic programming''. ICINCO and CAP, [http://www.grappa.univ-lille3.fr/~mary/paper/ldsfordp.pdf pdf]
* [[Sylvain Gelly]] ('''2007'''). ''A Contribution to Reinforcement Learning; Application to Computer Go.'' Ph.D. thesis, [http://www.lri.fr/~gelly/paper/SylvainGellyThesis.pdf pdf]
* [[Jean-Yves Audibert]], [[Rémi Munos]], [[Csaba Szepesvári]] ('''2007'''). ''Tuning Bandit Algorithms in Stochastic Environments''. [http://certis.enpc.fr/~audibert/ucb_alt.pdf pdf]
* [[Makoto Miwa]], [[Daisaku Yokoyama]], [[Takashi Chikayama]] ('''2007'''). ''Automatic Generation of Evaluation Features for Computer Game Players''. [http://cswww.essex.ac.uk/cig/2007/papers/2037.pdf pdf]
* [http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/d/Duan:Yong.html Yong Duan], [http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/c/Cui:Baoxia.html Baoxia Cui], [[Xinhe Xu]] ('''2007'''). ''State Space Partition for Reinforcement Learning Based on Fuzzy Min-Max Neural Network''. [http://www.informatik.uni-trier.de/~ley/db/conf/isnn/isnn2007-2.html#DuanCX07 ISNN 2007]
* [[Yasuhiro Osaki]], [[Kazutomo Shibahara]], [[Yasuhiro Tajima]], [[Yoshiyuki Kotani]] ('''2007'''). ''Reinforcement Learning of Evaluation Functions Using Temporal Difference-Monte Carlo learning method''. [[Conferences#GPW|12th Game Programming Workshop]]
* [[Igor Kononenko]], [http://www.informatik.uni-trier.de/%7Eley/db/indices/a-tree/k/Kukar:Matjaz.html Matjaž Kukar] ('''2007'''). ''[http://mldmbook.fri.uni-lj.si/ Machine Learning and Data Mining: Introduction to Principles and Algorithms]''.
* [[Krzysztof Krawiec]] ('''2007'''). ''[http://www.sciencedirect.com/science/article/pii/S0167865507002462 Generative Learning of Visual Concepts using Multiobjective Genetic Programming]''. [https://en.wikipedia.org/wiki/Pattern_Recognition_Letters Pattern Recognition Letters], Vol. 28, No. 16
* [[Simon Lucas]] ('''2007'''). ''[http://scholar.google.com/citations?view_op=view_citation&hl=de&user=Jz8DDVAAAAAJ&cstart=20&citation_for_view=Jz8DDVAAAAAJ:QIV2ME_5wuYC Learning to play Othello with N-tuple systems]''. [https://www.chatbots.org/journal/australian_journal_of_intelligent_information_processing_systems/ Australian Journal of Intelligent Information Processing Systems], Special Issue on Game Technology, Vol. 9, No. 4 » [[Othello]]
* [[Edward P. Manning]] ('''2007'''). ''[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4219046 Temporal Difference Learning of an Othello Evaluation Function for a Small Neural Network with Shared Weights]''. [[IEEE#CIG|IEEE Symposium on Computational Intelligence and AI in Games]] » [[Othello]]
* [[David J. Stracuzzi]] ('''2007'''). ''Randomized Feature Selection''. in [http://scholar.google.com/citations?user=Dzf46C8AAAAJ Huan Liu], [http://www.ar.sanken.osaka-u.ac.jp/~motoda/motopreg.html Hiroshi Motoda] (eds.) [http://www.crcpress.com/product/isbn/9781584888789 Computational Methods of Feature Selection]. [https://en.wikipedia.org/wiki/CRC_Press CRC Press], [http://www.stracuzzi.info/david/manuscripts/cmfs07-randomized.pdf pdf]
* [[Johannes Fürnkranz]] ('''2007'''). ''Recent advances in machine learning and game playing''. [http://www.oegai.at/journal.shtml ÖGAI Journal], Vol. 26, No. 2, Computer Game Playing, [https://www.ke.tu-darmstadt.de/~juffi/publications/ogai-07.pdf pdf]
'''2008'''
* [[Marco Block-Berlitz|Marco Block]], Maro Bader, [http://page.mi.fu-berlin.de/tapia/ Ernesto Tapia], Marte Ramírez, Ketill Gunnarsson, Erik Cuevas, Daniel Zaldivar, [[Raúl Rojas]] ('''2008'''). ''Using Reinforcement Learning in Chess Engines''. CONCIBE SCIENCE 2008, [http://www.micai.org/rcs/ Research in Computing Science]: Special Issue in Electronics and Biomedical Engineering, Computer Science and Informatics, ISSN:1870-4069, Vol. 35, pp. 31-40, [https://en.wikipedia.org/wiki/Guadalajara Guadalajara], Mexico, [http://page.mi.fu-berlin.de/block/concibe2008.pdf pdf]
* [[Sacha Droste]], [[Johannes Fürnkranz]] ('''2008'''). ''Learning of Piece Values for Chess Variants.'' Technical Report TUD–KE–2008-07, Knowledge Engineering Group, [[Darmstadt University of Technology|TU Darmstadt]], [http://www.ke.tu-darmstadt.de/publications/reports/tud-ke-2008-07.pdf pdf]
* [[Sacha Droste]], [[Johannes Fürnkranz]] ('''2008'''). ''Learning the Piece Values for three Chess Variants''. [[ICGA Journal#31_4|ICGA Journal, Vol 31, No. 4]]
* [[Richard Sutton]], [[Csaba Szepesvári]], [[Hamid Reza Maei]] ('''2008'''). ''A Convergent O(n) Algorithm for Off-policy Temporal-difference Learning with Linear Function Approximation'', [http://www.sztaki.hu/%7Eszcsaba/papers/gtdnips08.pdf pdf] (draft)
* [[Matej Guid]], [[Martin Možina]], [[Jana Krivec]], [[Aleksander Sadikov]], [[Ivan Bratko]] ('''2008'''). ''[http://link.springer.com/chapter/10.1007/978-3-540-87608-3_18 Learning Positional Features for Annotating Chess Games: A Case Study]''. [[CG 2008]], [http://www.ailab.si/matej/doc/Learning_Positional_Features-Case_Study.pdf pdf]
* [[Martin Možina]], [[Matej Guid]], [[Jana Krivec]], [[Aleksander Sadikov]], [[Ivan Bratko]] ('''2008'''). ''Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning''. 18th European Conference on Artificial Intelligence (ECAI 2008), Patras, Greece. [http://www.ailab.si/martin/abml/abml_expert_system_for_web.pdf pdf]
* [[Cécile Germain-Renaud]], [[Julien Pérez]], [[Balázs Kégl]], [[Charles Loomis]] ('''2008'''). ''Grid Differentiated Services: a Reinforcement Learning Approach''. In 8th [[IEEE]] Symposium on Cluster Computing and the Grid. Lyon, [http://hal.inria.fr/docs/00/28/78/26/PDF/RLccg08.pdf pdf]
* [[Yasuhiro Osaki]], [[Kazutomo Shibahara]], [[Yasuhiro Tajima]], [[Yoshiyuki Kotani]] ('''2008'''). ''An Othello Evaluation Function Based on Temporal Difference Learning using Probability of Winning''. [http://www.csse.uwa.edu.au/cig08/Proceedings/toc.html CIG'08], [http://www.csse.uwa.edu.au/cig08/Proceedings/papers/8010.pdf pdf]
* [[Antonio Fernández]], [[Antonio Salmerón]] ('''2008'''). ''[http://www.researchgate.net/publication/220647044_BayesChess_A_computer_chess_program_based_on_Bayesian_networks BayesChess: A computer chess program based on Bayesian networks]''. [https://en.wikipedia.org/wiki/Pattern_Recognition_Letters Pattern Recognition Letters], Vol. 29, No. 8
* [[Joaquin Vanschoren]], [[Bernhard Pfahringer]], [[Geoffrey Holmes]] ('''2008'''). ''Learning from the Past with Experiment Databases''. [http://www.informatik.uni-trier.de/~ley/db/conf/pricai/pricai2008.html#VanschorenPH08 PRICAI 2008], [https://lirias.kuleuven.be/bitstream/123456789/196273/1/pricai08.pdf pdf]
* [[Ilya Sutskever]], [[Vinod Nair]] ('''2008'''). ''Mimicking Go Experts with Convolutional Neural Networks''. [http://dblp.uni-trier.de/db/conf/icann/icann2008-2.html#SutskeverN08 ICANN 2008], [http://www.cs.utoronto.ca/~ilya/pubs/2008/go_paper.pdf pdf] » [[Go]]
* [[Andrew Cook]] ('''2008'''). ''Chunk Learning and Move Prompting: Making Moves in Chess''. Technical Report CSR-08-12, [https://en.wikipedia.org/wiki/University_of_Birmingham University of Birmingham]
* [[Byoung-Tak Zhang]] ('''2008'''). ''Hypernetworks: A molecular evolutionary architecture for cognitive learning and memory''. [[IEEE|IEEE Computational Intelligence Magazine]], Vol. 3, No. 3, [https://bi.snu.ac.kr/Publications/Journals/International/IEEE_Comp_Int_3_Zhang.pdf pdf]
* [[Maria Cutumisu]], [[Michael Bowling]], [[Duane Szafron]], [[Richard Sutton]] ('''2008'''). ''Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games''. [https://www.aaai.org/Library/AIIDE/aiide08contents.php Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference], [https://webdocs.cs.ualberta.ca/~duane/publications/pdf/2008aiide.pdf pdf]
'''2009'''
* [[Hamid Reza Maei]], [[Csaba Szepesvári]], [[Shalabh Bhatnagar]], [[Doina Precup]], [[David Silver]], [[Richard Sutton]] ('''2009'''). ''Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation.'' Accepted in Advances in Neural Information Processing Systems 22, Vancouver, BC. December 2009. MIT Press. [http://books.nips.cc/papers/files/nips22/NIPS2009_1121.pdf pdf]
* [[Joel Veness]], [[David Silver]], [[William Uther]], [[Alan Blair]] ('''2009'''). ''[http://papers.nips.cc/paper/3722-bootstrapping-from-game-tree-search Bootstrapping from Game Tree Search]''. [http://nips.cc/ Neural Information Processing Systems (NIPS), 2009], [http://books.nips.cc/papers/files/nips22/NIPS2009_0508.pdf pdf]
* [[Martin Možina]] ('''2009'''). ''Argument Based Machine Learning'', PhD Thesis, [http://www.ailab.si/martin/mozina_phd.pdf pdf]
* [[David Silver]] ('''2009'''). ''Reinforcement Learning and Simulation-Based Search''. Ph.D. thesis, [[University of Alberta]]. [http://webdocs.cs.ualberta.ca/~silver/David_Silver/Publications_files/thesis.pdf pdf]
* [[Omid David]], [[Jaap van den Herik]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2009'''). ''Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions''. [[ACM]] Genetic and Evolutionary Computation Conference ([http://www.sigevo.org/gecco-2009/ GECCO '09]), pp. 1483 - 1489, [https://en.wikipedia.org/wiki/Montreal Montreal], Canada, [http://www.omiddavid.com/pubs/gm-simul.pdf pdf]
* [[Omid David]] ('''2009'''). ''Genetic Algorithms Based Learning for Evolving Intelligent Organisms''. Ph.D. Thesis <ref>[[Dap Hartmann]] ('''2010'''). ''Mimicking the Black Box - Genetically evolving evaluation functions and search algorithms''. Review on Omid David's Ph.D. Thesis, [[ICGA Journal#33_1|ICGA Journal, Vol 33, No. 1]]</ref>
* [[Nur Merve Amil]], [[Nicolas Bredèche]], [[Christian Gagné]], [[Sylvain Gelly]], [[Marc Schoenauer]], [[Olivier Teytaud]] ('''2009'''). ''A Statistical Learning Perspective of Genetic Programming''. EuroGP 2009, [http://hal.inria.fr/docs/00/36/97/82/PDF/eurogp.pdf pdf]
* [[Richard Sutton]], [[Hamid Reza Maei]], [[Doina Precup]], [[Shalabh Bhatnagar]], [[David Silver]], [[Csaba Szepesvári]], [[Eric Wiewiora]]. ('''2009'''). ''Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation''. In Proceedings of the 26th International Conference on Machine Learning (ICML-09). [http://www.sztaki.hu/~szcsaba/papers/GTD-ICML09.pdf pdf]
* [[Mesut Kirci]], [[Jonathan Schaeffer]], [[Nathan Sturtevant]] ('''2009'''). ''Feature Learning Using State Differences''. [http://web.cs.du.edu/~sturtevant/papers/GGPfeatures.pdf pdf]
* [[David Silver]], [[Gerald Tesauro]] ('''2009'''). ''Monte-Carlo Simulation Balancing''. [http://www.informatik.uni-trier.de/~ley/db/conf/icml/icml2009.html#SilverT09 ICML 2009], [http://www.machinelearning.org/archive/icml2009/papers/500.pdf pdf] <ref>[http://videolectures.net/icml09_silver_mcsb/ Monte-Carlo Simulation Balancing - videolectures.net] by [[David Silver]]</ref>
* [[Broch Davison]] ('''2009'''). ''[http://www.enm.bris.ac.uk/teaching/projects/2008_09/bd5053/index.html Playing Chess with Matlab]''. M.Sc. thesis supervised by [http://www.bris.ac.uk/engineering/people/nello-cristianini/index.html Nello Cristianini], [http://www.enm.bris.ac.uk/teaching/projects/2008_09/bd5053/FinalReport.pdf pdf] <ref>[https://en.wikipedia.org/wiki/MATLAB MATLAB from Wikipedia]</ref>
* [[Marcin Szubert]], [[Wojciech Jaśkowski]], [[Krzysztof Krawiec]] ('''2009'''). ''Coevolutionary Temporal Difference Learning for Othello''. [[IEEE#CIG|IEEE Symposium on Computational Intelligence and Games]], [http://www.cs.put.poznan.pl/wjaskowski/pub/papers/szubert09coevolutionary.pdf pdf] » [[Othello]]
* [[Mark Levene]], [[Trevor Fenner]] ('''2009'''). ''A Methodology for Learning Players' Styles from Game Records''. [http://arxiv.org/abs/0904.2595v1 arXiv:0904.2595v1]
* [[Mathematician#THastie|Trevor Hastie]], [[Mathematician#RTibshirani|Robert Tibshirani]], [https://en.wikipedia.org/wiki/Jerome_H._Friedman Jerome Friedman] ('''2009'''). ''[http://www.springer.com/book/9780387848570 The Elements of Statistical Learning: Data Mining, Inference, and Prediction]''. Second Edition, [https://de.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
==2010 ...==
* [[Jacek Mańdziuk]] ('''2010'''). ''[http://link.springer.com/book/10.1007%2F978-3-642-11678-0 Knowledge-Free and Learning-Based Methods in Intelligent Game Playing]''. [http://link.springer.com/bookseries/7092 Studies in Computational Intelligence], Vol. 276, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [[Joel Veness]], [[Kee Siong Ng]], [[Marcus Hutter]], [[David Silver]] ('''2010'''). ''Reinforcement Learning via AIXI Approximation''. Association for the Advancement of Artificial Intelligence (AAAI), [http://jveness.info/publications/veness_rl_via_aixi_approx.pdf pdf]
* [[Omid David]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2010'''). ''[http://www.springerlink.com/content/3346t8432n718821 Expert-Driven Genetic Algorithms for Simulating Evaluation Functions]''. [http://www.omiddavid.com/pubs/expert-driven.pdf pdf]
* [[Omid David]], [[Nathan S. Netanyahu]], Yoav Rosenberg, Moshe Shimoni ('''2010'''). ''Genetic Algorithms for Automatic Classification of Moving Objects''. [[ACM]] Genetic and Evolutionary Computation Conference ([http://www.sigevo.org/gecco-2010/ GECCO '10]), [https://en.wikipedia.org/wiki/Portland,_Oregon Portland, OR], [http://www.omiddavid.com/pubs/object-classification.pdf pdf]
* [[Omid David]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2010'''). ''Genetic Algorithms for Automatic Search Tuning''. [[ICGA Journal#33_2|ICGA Journal, Vol 33, No. 2]]
* [[Mesut Kirci]] ('''2010'''). ''Feature Learning using State Differences''. Master's thesis, Department of Computing Science, [[University of Alberta]], [http://repository.library.ualberta.ca/dspace/bitstream/10048/1011/1/kirci_mesut_spring+2010.pdf pdf] » [[General Game Playing]]
* [[Amine Bourki]], [[Matthieu Coulm]], [[Philippe Rolet]], [[Olivier Teytaud]], [[Paul Vayssière]] ('''2010'''). ''[http://hal.inria.fr/inria-00467796/en/ Parameter Tuning by Simple Regret Algorithms and Multiple Simultaneous Hypothesis Testing]''. [http://hal.inria.fr/docs/00/46/77/96/PDF/tosubmit.pdf pdf]
* [[Julien Pérez]], [[Cécile Germain-Renaud]], [[Balázs Kégl]], [[Charles Loomis]] ('''2010'''). ''Multi-objective Reinforcement Learning for Responsive Grids''. In The Journal of Grid Computing. [http://hal.archives-ouvertes.fr/docs/00/49/15/60/PDF/RLGrid_JGC09_V7.pdf pdf]
* [[Jean-Yves Audibert]] ('''2010'''). ''PAC-Bayesian aggregation and multi-armed bandits''. Habilitation thesis, [http://fr.wikipedia.org/wiki/Universit%C3%A9_Paris-Est Université Paris Est], [http://certis.enpc.fr/~audibert/Mes%20articles/hdr.pdf pdf], [http://certis.enpc.fr/~audibert/Mes%20articles/hdrSlides.pdf slides as pdf]
* [[Hamid Reza Maei]], [[Richard Sutton]] ('''2010'''). ''[http://www.incompleteideas.net/sutton/publications.html#GQ GQ(λ): A general gradient algorithm for temporal-difference prediction learning with eligibility traces]''. In Proceedings of the Third Conference on Artificial General Intelligence
* [http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/w/Waledzik:Karol.html Karol Walędzik], [[Jacek Mańdziuk]] ('''2010'''). ''The Layered Learning method and its Application to Generation of Evaluation Functions for the Game of Checkers''. [http://www.informatik.uni-trier.de/~ley/db/conf/ppsn/ppsn2010-2.html#WaledzikM10 11. PPSN], [http://www.mini.pw.edu.pl/~mandziuk/PRACE/PPSN10.pdf pdf] » [[Checkers]]
* [[Krzysztof Krawiec]], [[Marcin Szubert]] ('''2010'''). ''[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5586054 Coevolutionary Temporal Difference Learning for small-board Go]''. [[IEEE#EC|IEEE Congress on Evolutionary Computation]] » [[Go]]
* [[Edward P. Manning]] ('''2010'''). ''[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5409565 Using Resource-Limited Nash Memory to Improve an Othello Evaluation Function]''. [[IEEE#TOCIAIGAMES|IEEE Transactions on Computational Intelligence and AI in Games]], Vol. 2, No. 1 » [[Othello]]
* [[Edward P. Manning]] ('''2010'''). ''[http://dl.acm.org/citation.cfm?id=1830667 Coevolution in a Large Search Space using Resource-limited Nash Memory]''. [http://www.informatik.uni-trier.de/~ley/db/conf/gecco/gecco2010.html#Manning10 GECCO '10] » [[Othello]]
* [[Marco Wiering]] ('''2010'''). ''[https://scholar.google.com/citations?view_op=view_citation&hl=en&user=xVas0I8AAAAJ&cstart=20&citation_for_view=xVas0I8AAAAJ:_kc_bZDykSQC Self-play and using an expert to learn to play backgammon with temporal difference learning]''. [http://www.scirp.org/journal/jilsa/ Journal of Intelligent Learning Systems and Applications], Vol. 2, No. 2
'''2011'''
* [[Joel Veness]] ('''2011'''). ''Approximate Universal Artificial Intelligence and Self-Play Learning for Games''. Ph.D. thesis, [https://en.wikipedia.org/wiki/University_of_New_South_Wales University of New South Wales], supervisors: [[Kee Siong Ng]], [[Marcus Hutter]], [[Alan Blair]], [[William Uther]], [[John Lloyd]]; [http://jveness.info/publications/veness_phd_thesis_final.pdf pdf]
* [[Mesut Kirci]], [[Nathan Sturtevant]], [[Jonathan Schaeffer]] ('''2011'''). ''A GGP Feature Learning Algorithm''. [http://www.informatik.uni-trier.de/~ley/db/journals/ki/ki25.html#KirciSS11 KI 25(1)]: 35-42, [http://web.cs.du.edu/~sturtevant/papers/FeatureLearning.pdf pdf] » [[General Game Playing]]
* [[I-Chen Wu]], [[Hsin-Ti Tsai]], [[Hung-Hsuan Lin]], [[Yi-Shan Lin]], [[Chieh-Min Chang]], [[Ping-Hung Lin]] ('''2011'''). ''[https://www.conftool.net/acg13/index.php?page=browseSessions&form_session=5 Temporal Difference Learning for Connect6]''. [[Advances in Computer Games 13]]
* [[Tomoyuki Kaneko]], [[Kunihito Hoki]] ('''2011'''). ''Analysis of Evaluation-Function Learning by Comparison of Sibling Nodes''. [[Advances in Computer Games 13]]
* [[Jiao Wang]], [[Shiyuan Li]], [[Jitong Chen]], [[Xin Wei]], [[Huizhan Lv]], [[Xinhe Xu]] ('''2011'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-642-31866-5_10?LI=true#page-1 4*4-Pattern and Bayesian Learning in Monte-Carlo Go]''. [[Advances in Computer Games 13]]
* [[Charles Elkan]] ('''2011'''). ''Reinforcement Learning with a Bilinear Q Function''. [http://www.informatik.uni-trier.de/~ley/db/conf/ewrl/ewrl2011.html#Elkan11 EWRL 2011]
* [[Krzysztof Krawiec]], [[Marcin Szubert]] ('''2011'''). ''Learning N-Tuple Networks for Othello by Coevolutionary Gradient Search''. [http://www.informatik.uni-trier.de/~ley/db/conf/gecco/gecco2011.html#KrawiecS11 GECCO 2011], [http://www.cs.put.poznan.pl/mszubert/pub/krawiec2011gecco.pdf pdf]
* [[Krzysztof Krawiec]], [[Wojciech Jaśkowski]], [[Marcin Szubert]] ('''2011'''). ''[http://www.degruyter.com/view/j/amcs.2011.21.issue-4/v10006-011-0057-3/v10006-011-0057-3.xml Evolving small-board Go players using Coevolutionary Temporal Difference Learning with Archives]''. [http://www.degruyter.com/view/j/amcs Applied Mathematics and Computer Science], Vol. 21, No. 4
* [[Marcin Szubert]], [[Wojciech Jaśkowski]], [[Krzysztof Krawiec]] ('''2011'''). ''Learning Board Evaluation Function for Othello by Hybridizing Coevolution with Temporal Difference Learning''. [http://control.ibspan.waw.pl:3000/mainpage Control and Cybernetics], Vol. 40, No. 3, [http://www.cs.put.poznan.pl/wjaskowski/pub/papers/szubert2011learning.pdf pdf] » [[Othello]]
* [[Hamid Reza Maei]] ('''2011'''). ''Gradient Temporal-Difference Learning Algorithms''. Ph.D. thesis, [[University of Alberta]], advisor [[Richard Sutton]], [http://webdocs.cs.ualberta.ca/~sutton/papers/maei-thesis-2011.pdf pdf]
'''2012'''
* [[Marco Wiering]], [http://martijnvanotterlo.nl/ Martijn Van Otterlo] ('''2012'''). ''[https://scholar.google.com/citations?view_op=view_citation&hl=en&user=xVas0I8AAAAJ&citation_for_view=xVas0I8AAAAJ:abG-DnoFyZgC Reinforcement learning: State-of-the-art]''. [http://link.springer.com/book/10.1007/978-3-642-27645-3 Adaptation, Learning, and Optimization, Vol. 12], [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
: [[István Szita]] ('''2012'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-642-27645-3_17 Reinforcement Learning in Games]''. Chapter 17
* [http://dblp.uni-trier.de/pers/hd/d/Dries:Sjoerd_van_den Sjoerd van den Dries], [[Marco Wiering]] ('''2012'''). ''[https://scholar.google.com/citations?view_op=view_citation&hl=en&user=xVas0I8AAAAJ&cstart=40&citation_for_view=xVas0I8AAAAJ:P5F9QuxV20EC Neural-fitted TD-leaf learning for playing Othello with structured neural networks]''. [[IEEE#NN|IEEE Transactions on Neural Networks and Learning Systems]], Vol. 23, No. 11
* [[Amir Ban]] ('''2012'''). ''[http://www.ratio.huji.ac.il/node/2362 Automatic Learning of Evaluation, with Applications to Computer Chess]''. Discussion Paper 613, [https://en.wikipedia.org/wiki/Hebrew_University_of_Jerusalem The Hebrew University of Jerusalem] - Center for the Study of Rationality, [https://en.wikipedia.org/wiki/Givat_Ram Givat Ram]
* [[Adrien Couetoux]], [[Olivier Teytaud]], [[Hassen Doghmen]] ('''2012'''). ''Learning a Move-Generator for Upper Confidence Trees''. [http://ics2012.ndhu.edu.tw/ ICS 2012], [https://en.wikipedia.org/wiki/Hualien_City Hualien], [https://en.wikipedia.org/wiki/Taiwan Taiwan], December 2012 » [[UCT]]
* [[Robert Schapire]], [[Yoav Freund]] ('''2012'''). ''[http://mitpress.mit.edu/books/boosting Boosting: Foundations and Algorithms]''. [https://en.wikipedia.org/wiki/MIT_Press MIT Press]
* [[Arthur Guez]], [[David Silver]], [[Peter Dayan]] ('''2012'''). ''Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search''. [http://papers.nips.cc/book/advances-in-neural-information-processing-systems-25-2012 NIPS 2012], [https://papers.nips.cc/paper/4767-efficient-bayes-adaptive-reinforcement-learning-using-sample-based-search.pdf pdf]
* [[Peter Dayan]] ('''2012'''). ''How to set the switches on this thing''. [http://www.sciencedirect.com/science/journal/09594388 Current Opinion in Neurobiology], Vol. 22, [http://www.gatsby.ucl.ac.uk/~dayan/papers/dayanswitch2012.pdf pdf]
'''2013'''
* [[Arthur Guez]], [[David Silver]], [[Peter Dayan]] ('''2013'''). ''Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research Journal of Artificial Intelligence Research], Vol. 48, [https://www.jair.org/media/4117/live-4117-7507-jair.pdf pdf]
* [[Katja Grace]] ('''2013'''). ''Algorithmic Progress in Six Domains''. Technical report 2013-3, [https://en.wikipedia.org/wiki/Machine_Intelligence_Research_Institute Machine Intelligence Research Institute], [https://en.wikipedia.org/wiki/Berkeley,_California Berkeley, CA], [http://intelligence.org/files/AlgorithmicProgress.pdf pdf], 5 [[Games|Game Playing]], 5.1 [[Chess]], 5.2 [[Go]], 9 [[Learning|Machine Learning]]
* [[Marcin Szubert]], [[Wojciech Jaśkowski]], [[Paweł Liskowski]], [[Krzysztof Krawiec]] ('''2013'''). ''Shaping Fitness Function for Evolutionary Learning of Game Strategies''. [http://www.informatik.uni-trier.de/~ley/db/conf/gecco/gecco2013.html#SzubertJLK13 GECCO 2013], [http://www.cs.put.poznan.pl/wjaskowski/pub/papers/szubert2013shaping.pdf pdf]
* [[Marcin Szubert]], [[Wojciech Jaśkowski]], [[Krzysztof Krawiec]] ('''2013'''). ''[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6504736 On Scalability, Generalization, and Hybridization of Coevolutionary Learning: a Case Study for Othello]''. [[IEEE#TOCIAIGAMES|IEEE Transactions on Computational Intelligence and AI in Games]], Vol. 5, No. 3 » [[Othello]]
* [http://dblp.uni-trier.de/pers/hd/r/Ree:M=_van_der Michiel van der Ree], [[Marco Wiering]] ('''2013'''). ''[https://scholar.google.com/citations?view_op=view_citation&hl=en&user=xVas0I8AAAAJ&cstart=60&pagesize=80&citation_for_view=xVas0I8AAAAJ:K3LRdlH-MEoC Reinforcement Learning in the Game of Othello: Learning Against a Fixed Opponent and Learning from Self-Play]''. [http://dblp.uni-trier.de/db/conf/adprl/adprl2013.html#ReeW13 ADPRL 2013]
* [http://dblp.uni-trier.de/pers/hd/b/Bom:Luuk Luuk Bom], [http://dblp.uni-trier.de/pers/hd/h/Henken:Ruud Ruud Henken], [[Marco Wiering]] ('''2013'''). ''[https://scholar.google.com/citations?view_op=view_citation&hl=en&user=xVas0I8AAAAJ&cstart=40&citation_for_view=xVas0I8AAAAJ:l7t_Zn2s7bgC Reinforcement Learning to Train Ms. Pac-Man Using Higher-order Action-relative Inputs]''. [http://dblp.uni-trier.de/db/conf/adprl/adprl2013.html#BomHW13 ADPRL 2013] <ref>[https://en.wikipedia.org/wiki/Ms._Pac-Man Ms. Pac-Man from Wikipedia]</ref>
* [[Peter Auer]], [[Marcus Hutter]], [[Laurent Orseau]] ('''2013'''). ''[http://drops.dagstuhl.de/opus/volltexte/2013/4340/ Reinforcement Learning]''. [http://dblp.uni-trier.de/db/journals/dagstuhl-reports/dagstuhl-reports3.html#AuerHO13 Dagstuhl Reports, Vol. 3, No. 8], DOI: [http://drops.dagstuhl.de/opus/volltexte/2013/4340/ 10.4230/DagRep.3.8.1], URN: [http://drops.dagstuhl.de/opus/volltexte/2013/4340/ urn:nbn:de:0030-drops-43409]
* [[Volodymyr Mnih]], [[Koray Kavukcuoglu]], [[David Silver]], [[Alex Graves]], [[Ioannis Antonoglou]], [[Daan Wierstra]], [[Martin Riedmiller]] ('''2013'''). ''Playing Atari with Deep Reinforcement Learning''. [http://arxiv.org/abs/1312.5602 arXiv:1312.5602] <ref>[http://www.nervanasys.com/demystifying-deep-reinforcement-learning/ Demystifying Deep Reinforcement Learning] by [http://www.nervanasys.com/author/tambet/ Tambet Matiisen], [http://www.nervanasys.com/ Nervana], December 21, 2015</ref> <ref>[http://www.google.com/patents/US20150100530 Patent US20150100530 - Methods and apparatus for reinforcement learning - Google Patents]</ref>
'''2014'''
* [[Omid David]], [[Jaap van den Herik]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2014'''). ''Genetic Algorithms for Evolving Computer Chess Programs''. [[IEEE#EC|IEEE Transactions on Evolutionary Computation]], [http://www.genetic-programming.org/hc2014/David-Paper.pdf pdf] <ref>[http://www.liacs.nl/nieuws/jaap-van-den-herik-wint-humies-award-2014/ Jaap van den Herik wint Humies Award 2014 - LIACS - Leiden Institute of Advanced Computer Science]</ref>
* [[Wojciech Jaśkowski]], [[Marcin Szubert]], [[Paweł Liskowski]] ('''2014'''). ''Multi-Criteria Comparison of Coevolution and Temporal Difference Learning on Othello''. [http://www.evostar.org/2014/ EvoApplications 2014], [http://www.springer.com/computer/theoretical+computer+science/book/978-3-662-45522-7 Springer, volume 8602] » [[Othello]]
* [[Marcin Szubert]], [[Wojciech Jaśkowski]] ('''2014'''). ''Temporal Difference Learning of N-Tuple Networks for the Game 2048''. [[IEEE#CIG|IEEE Conference on Computational Intelligence and Games]], [http://www.cs.put.poznan.pl/mszubert/pub/szubert2014cig.pdf pdf] <ref>[https://en.wikipedia.org/wiki/2048_%28video_game%29 2048 (video game) from Wikipedia]</ref>
* [[Marcin Szubert]] ('''2014'''). ''Coevolutionary Shaping for Reinforcement Learning''. Ph.D. thesis, [https://en.wikipedia.org/wiki/Pozna%C5%84_University_of_Technology Poznań University of Technology], supervisor [[Krzysztof Krawiec]], co-supervisor [[Wojciech Jaśkowski]], [http://www.cs.put.poznan.pl/mszubert/pub/phdthesis.pdf pdf]
* [[Wojciech Jaśkowski]] ('''2014'''). ''Systematic n-Tuple Networks for Othello Position Evaluation''. [[ICGA Journal#37_2|ICGA Journal, Vol. 37, No. 2]], [http://www.cs.put.poznan.pl/wjaskowski/pub/papers/jaskowski2014ICGAsystematic.pdf preprint as pdf] » [[Othello]]
* [[Christopher Clark]], [[Amos Storkey]] ('''2014'''). ''Teaching Deep Convolutional Neural Networks to Play Go''. [http://arxiv.org/abs/1412.3409 arXiv:1412.3409] » [[Neural Networks]] <ref>[http://computer-go.org/pipermail/computer-go/2014-December/007010.html Teaching Deep Convolutional Neural Networks to Play Go] by [[Hiroshi Yamashita]], [http://computer-go.org/pipermail/computer-go/ The Computer-go Archives], December 14, 2014</ref> <ref>[http://www.talkchess.com/forum/viewtopic.php?t=54663 Teaching Deep Convolutional Neural Networks to Play Go] by [[Michel Van den Bergh]], [[CCC]], December 16, 2014</ref> <ref>[https://en.wikipedia.org/wiki/Convolutional_neural_network Convolutional neural network from Wikipedia]</ref>
* [[Chris J. Maddison]], [[Shih-Chieh Huang|Aja Huang]], [[Ilya Sutskever]], [[David Silver]] ('''2014'''). ''Move Evaluation in Go Using Deep Convolutional Neural Networks''. [http://arxiv.org/abs/1412.6564v1 arXiv:1412.6564v1]
* [[I-Chen Wu]], [[Kun-Hao Yeh]], [[Chao-Chin Liang]], [[Chia-Chuan Chang]], [[Han Chiang]] ('''2014'''). ''Multi-Stage Temporal Difference Learning for 2048''. [[TAAI 2014]], best paper award <ref>[http://taai2014.ntust.edu.tw/best-paper-award/ Best Paper Awards | TAAI 2014]</ref>
* [[Mathematician#ROrtner|Ronald Ortner]], [[Mathematician#DRyabko|Daniil Ryabko]], [[Peter Auer]], [[Rémi Munos]] ('''2014'''). ''Regret bounds for restless Markov bandits''. [https://en.wikipedia.org/wiki/Theoretical_Computer_Science_%28journal%29 Theoretical Computer Science] 558, [http://daniil.ryabko.net/mabajr.pdf pdf]
==2015 ...==
* [[Volodymyr Mnih]], [[Koray Kavukcuoglu]], [[David Silver]], [[Andrei A. Rusu]], [[Joel Veness]], [[Marc G. Bellemare]], [[Alex Graves]], [[Martin Riedmiller]], [[Andreas K. Fidjeland]], [[Georg Ostrovski]], [[Stig Petersen]], [[Charles Beattie]], [[Amir Sadik]], [[Ioannis Antonoglou]], [[Helen King]], [[Dharshan Kumaran]], [[Daan Wierstra]], [[Shane Legg]], [[Demis Hassabis]] ('''2015'''). ''[http://www.nature.com/nature/journal/v518/n7540/abs/nature14236.html Human-level control through deep reinforcement learning]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 518
* [[Tobias Graf]], [[Marco Platzner]] ('''2015'''). ''Adaptive Playouts in Monte Carlo Tree Search with Policy Gradient Reinforcement Learning''. [[Advances in Computer Games 14]]
* [[Yuichiro Sato]], [[Hiroyuki Iida]], [[Jaap van den Herik]] ('''2015'''). ''Transfer Learning by Inductive Logic Programming''. [[Advances in Computer Games 14]]
* [[Kokolo Ikeda]], [[Takanari Shishido]], [[Simon Viennot]] ('''2015'''). ''Machine-Learning of Shape Names for the Game of Go''. [[Advances in Computer Games 14]]
* [[Arun Nair]], [[Praveen Srinivasan]], [[Sam Blackwell]], [[Cagdas Alcicek]], [[Rory Fearon]], [[Alessandro De Maria]], [[Veda Panneershelvam]], [[Mustafa Suleyman]], [[Charles Beattie]], [[Stig Petersen]], [[Shane Legg]], [[Volodymyr Mnih]], [[Koray Kavukcuoglu]], [[David Silver]] ('''2015'''). ''Massively Parallel Methods for Deep Reinforcement Learning''. [http://arxiv.org/abs/1507.04296 arXiv:1507.04296]
* [[Matthew Lai]] ('''2015'''). ''Giraffe: Using Deep Reinforcement Learning to Play Chess''. M.Sc. thesis, [https://en.wikipedia.org/wiki/Imperial_College_London Imperial College London], [http://arxiv.org/abs/1509.01549v1 arXiv:1509.01549v1] » [[Giraffe]]
* [[Hado van Hasselt]], [[Arthur Guez]], [[David Silver]] ('''2015'''). ''Deep Reinforcement Learning with Double Q-learning''. [http://arxiv.org/abs/1509.06461 arXiv:1509.06461]
* [[Tom Schaul]], [[John Quan]], [[Ioannis Antonoglou]], [[David Silver]] ('''2015'''). ''Prioritized Experience Replay''. [http://arxiv.org/abs/1511.05952 arXiv:1511.05952]
* [[Miroslav Kubat]] ('''2015'''). ''[http://www.springer.com/us/book/9783319200095 An Introduction to Machine Learning]''. [https://de.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [[Christian Wirth]], [[Johannes Fürnkranz]] ('''2015'''). ''[http://ieeexplore.ieee.org/document/6861960/ On Learning From Game Annotations]''. [[IEEE#TOCIAIGAMES|IEEE Transactions on Computational Intelligence and AI in Games]], Vol. 7, No. 3
'''2016'''
* [[Dharshan Kumaran]], [[Demis Hassabis]], [[James L. McClelland]] ('''2016'''). ''What learning systems do intelligent agents need? Complementary Learning Systems Theory Updated''. [https://en.wikipedia.org/wiki/Trends_in_Cognitive_Sciences Trends in Cognitive Sciences], Vol. 20, No. 7, [https://drive.google.com/file/d/0B-Nvsz4idhaeVEZYMEVaWkFjLVU/view pdf]
* [[Ziyu Wang]], [[Nando de Freitas]], [[Marc Lanctot]] ('''2016'''). ''Dueling Network Architectures for Deep Reinforcement Learning''. [http://arxiv.org/abs/1511.06581 arXiv:1511.06581]
* [[Jialin Liu]], [[Olivier Teytaud]], [[Tristan Cazenave]] ('''2016'''). ''Fast seed-learning algorithms for games''. [[CG 2016]]
* [[Omid David|Omid E. David]], [[Nathan S. Netanyahu]], [[Lior Wolf]] ('''2016'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-319-44781-0_11 DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess]''. [http://icann2016.org/ ICAAN 2016], [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 9887, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer], [http://www.cs.tau.ac.il/~wolf/papers/deepchess.pdf pdf preprint] » [[DeepChess]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=61748 DeepChess: Another deep-learning based chess program] by [[Matthew Lai]], [[CCC]], October 17, 2016</ref> <ref>[http://icann2016.org/index.php/conference-programme/recipients-of-the-best-paper-awards/ ICANN 2016 | Recipients of the best paper awards]</ref>
* [https://www.linkedin.com/in/ian-goodfellow-b7187213 Ian Goodfellow], [https://en.wikipedia.org/wiki/Yoshua_Bengio Yoshua Bengio], [https://www.linkedin.com/in/aaron-courville-53a63459 Aaron Courville] ('''2016'''). ''[http://www.deeplearningbook.org/ Deep Learning]''. [https://en.wikipedia.org/wiki/MIT_Press MIT Press]
* [[Max Jaderberg]], [[Volodymyr Mnih]], [[Wojciech Marian Czarnecki]], [[Tom Schaul]], [[Joel Z. Leibo]], [[David Silver]], [[Koray Kavukcuoglu]] ('''2016'''). ''Reinforcement Learning with Unsupervised Auxiliary Tasks''. [https://arxiv.org/abs/1611.05397v1 arXiv:1611.05397v1]
'''2017'''
* [[Muthuraman Chidambaram]], [[Yanjun Qi]] ('''2017'''). ''Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently''. [https://arxiv.org/abs/1702.06762v1 arXiv:1702.06762v1] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=63252 Using GAN to play chess] by Evgeniy Zheltonozhskiy, [[CCC]], February 23, 2017</ref> » [[Neural Networks]]
* [[Johannes Fürnkranz]] ('''2017'''). ''Machine Learning and Game Playing''. in [https://en.wikipedia.org/wiki/Claude_Sammut Claude Sammut], [https://en.wikipedia.org/wiki/Geoff_Webb Geoffrey I. Webb] (eds) ('''2017'''). ''[https://link.springer.com/referencework/10.1007%2F978-1-4899-7687-1 Encyclopedia of Machine Learning and Data Mining]''. [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer], [https://en.wikipedia.org/wiki/Boston Boston, MA]
* [[David Silver]], [[Thomas Hubert]], [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Matthew Lai]], [[Arthur Guez]], [[Marc Lanctot]], [[Laurent Sifre]], [[Dharshan Kumaran]], [[Thore Graepel]], [[Timothy Lillicrap]], [[Karen Simonyan]], [[Demis Hassabis]] ('''2017'''). ''Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm''. [https://arxiv.org/abs/1712.01815 arXiv:1712.01815] » [[AlphaZero]]

=Forum Posts=
==1998 ...==
* [https://www.stmintz.com/ccc/index.php?id=17861 Book learning and rating bias] by [[Don Dailey]], [[CCC]], May 01, 1998
* [https://www.stmintz.com/ccc/index.php?id=25754 BookLearning Under the Microscope!!!] by Robert Henry Durrett, [[CCC]], August 31, 1998
* [https://www.stmintz.com/ccc/index.php?id=37968 Book learning?] by [[Werner Inmann]], [[CCC]], December 31, 1998
* [https://www.stmintz.com/ccc/index.php?id=68359 Book learning] by [[James Robertson]], [[CCC]], September 12, 1999
==2000 ...==
* [https://www.stmintz.com/ccc/index.php?id=226258 question about book and learning] by [[Uri Blass]], [[CCC]], April 26, 2002
* [https://www.stmintz.com/ccc/index.php?id=351488 Time to implement Learning] by [[Tom Likens]], [[CCC]], February 26, 2004
==2005 ...==
* [http://www.open-aurec.com/wbforum/viewtopic.php?f=4&t=4835 RomiChess && learning or the emperor has no clothes] by [[Michael Sherwin]], [[Computer Chess Forums|Winboard Programming Forum]], May 19, 2006
* [http://www.talkchess.com/forum/viewtopic.php?t=19381 learning] by Jim, [[CCC]], February 03, 2008
* [http://www.talkchess.com/forum/viewtopic.php?t=20549 Information on engines with learning capabilities] by [[Martin Thoresen]], [[CCC]], April 06, 2008
* [http://www.talkchess.com/forum/viewtopic.php?t=29056 naive bayes classifier] by [[Don Dailey]], [[CCC]], July 21, 2009 <ref>[https://en.wikipedia.org/wiki/Naive_Bayes_classifier Naive Bayes classifier from Wikipedia]</ref>
==2010 ...==
* [https://groups.google.com/d/msg/computer-go-archive/JrrSovvgTV0/UbPOufyTApQJ [Computer-go] learning patterns for mc go] by [[Hendrik Baier]], [https://groups.google.com/forum/#!forum/computer-go-archive Computer Go Archive], April 26, 2010
* [http://www.talkchess.com/forum/viewtopic.php?t=37062 Positional learning] by [[Ben-Hur Carlos Vieira Langoni Junior]], [[CCC]], December 13, 2010
* [http://www.open-chess.org/viewtopic.php?f=5&t=1954 Ban: Automatic Learning of Evaluation [...]] by [[Mark Watkins|BB+]], [[Computer Chess Forums|OpenChess Forum]], May 10, 2012 <ref>[[Amir Ban]] ('''2012'''). ''[http://www.ratio.huji.ac.il/node/2362 Automatic Learning of Evaluation, with Applications to Computer Chess]''. Discussion Paper 613, [https://en.wikipedia.org/wiki/Hebrew_University_of_Jerusalem The Hebrew University of Jerusalem] - Center for the Study of Rationality, [https://en.wikipedia.org/wiki/Givat_Ram Givat Ram]</ref>
* [http://computer-go.org/pipermail/computer-go/2014-December/007010.html Teaching Deep Convolutional Neural Networks to Play Go] by [[Hiroshi Yamashita]], [http://computer-go.org/pipermail/computer-go/ The Computer-go Archives], December 14, 2014 <ref>[[Christopher Clark]], [[Amos Storkey]] ('''2014'''). ''Teaching Deep Convolutional Neural Networks to Play Go''. [http://arxiv.org/abs/1412.3409 arXiv:1412.3409]</ref>
* [http://www.talkchess.com/forum/viewtopic.php?t=54663 Teaching Deep Convolutional Neural Networks to Play Go] by [[Michel Van den Bergh]], [[CCC]], December 16, 2014 <ref>[[Chris J. Maddison]], [[Shih-Chieh Huang|Aja Huang]], [[Ilya Sutskever]], [[David Silver]] ('''2014'''). ''Move Evaluation in Go Using Deep Convolutional Neural Networks''. [http://arxiv.org/abs/1412.6564v1 arXiv:1412.6564v1]</ref>
==2015 ...==
* [http://www.talkchess.com/forum/viewtopic.php?t=56168 Piece weights with regression analysis (in Russian)] by [[Vladimir Medvedev]], [[CCC]], April 30, 2015 » [[Point Value by Regression Analysis]]
* [http://www.talkchess.com/forum/viewtopic.php?t=56313 Position learning and opening books] by Forrest Hoch, [[CCC]], May 11, 2015
* [http://www.talkchess.com/forum/viewtopic.php?t=61861 A database for learning evaluation functions] by [[Álvaro Begué]], [[CCC]], October 28, 2016 » [[Automated Tuning]], [[Evaluation]], [[Texel's Tuning Method]]

=External Links=
* [https://en.wikipedia.org/wiki/Learning Learning from Wikipedia]
* [https://en.wikipedia.org/wiki/Classical_conditioning Classical conditioning from Wikipeadia]
* [https://en.wikipedia.org/wiki/Learnable_Evolution_Model Learnable Evolution Model from Wikipedia]
* [https://en.wikipedia.org/wiki/Monkey_see,_monkey_do Monkey see Monkey do from Wikipeadia]
* [https://en.wikipedia.org/wiki/Observational_learning Observational learning from Wikipeadia]
* [https://en.wikipedia.org/wiki/Rote_learning Rote learning from Wikipeadia]
==Machine Learning==
* [https://en.wikipedia.org/wiki/Machine_learning Machine learning from Wikipeadia]
* [http://satirist.org/learn-game/ Machine Learning in Games] by [[Jay Scott]]
* [http://machine-learning.martinsewell.com/ Machine Learning] by [http://www.martinsewell.com/ Martin Sewell]
* [https://en.wikipedia.org/wiki/List_of_machine_learning_concepts List of machine learning concepts from Wikipedia]
* [https://en.wikipedia.org/wiki/Apprenticeship_learning Apprenticeship learning from Wikipedia]
* [https://en.wikipedia.org/wiki/Ensemble_learning Ensemble learning from Wikipedia]
* [https://en.wikipedia.org/wiki/Explanation-based_learning Explanation-based learning from Wikipedia]
* [https://en.wikipedia.org/wiki/Meta_learning_%28computer_science%29 Meta Learning from Wikipedia]
* [https://en.wikipedia.org/wiki/Online_machine_learning Online Machine Learning from Wikipedia]
* [https://en.wikipedia.org/wiki/Probably_approximately_correct_learning PAC Learning from Wikipedia]
* [http://www.idsia.ch/%7Ejuergen/learningrobots.html Learning Robots / Robot Learning] by [[Jürgen Schmidhuber]]
* [https://en.wikipedia.org/wiki/Similarity_learning Similarity learning from Wikipedia]
* [http://www.idsia.ch/~juergen/unilearn.html Universal Learning Machines - Optimal Universal AI] by [[Jürgen Schmidhuber]]
* [http://archive.ics.uci.edu/ml/ UCI Machine Learning Repository] from [https://en.wikipedia.org/wiki/University_of_California,_Irvine University of California, Irvine]
* [https://csforallteachers.org/group/nmcs4all NMCS4All]: Machine Learning by [[Mathematician#DHAckley|David H. Ackley]], [https://en.wikipedia.org/wiki/YouTube YouTube] Video
: {{#evu:https://www.youtube.com/watch?v=OQsn1c92pdY|alignment=left|valignment=top}}
==AI==
* [http://www.cs.cf.ac.uk/Dave/AI2/AI_notes.html Artificial Intelligence II] by [http://blogs.gartner.com/nikos_drakos/ Nikos Drakos], Computer Based Learning Unit, [https://en.wikipedia.org/wiki/University_of_Leeds University of Leeds]
: [http://www.cs.cf.ac.uk/Dave/AI2/node130.html#SECTION000150000000000000000 Learning I]
: [http://www.cs.cf.ac.uk/Dave/AI2/node143.html#SECTION000160000000000000000 Learning II]
* [http://www.aihorizon.com/essays/generalai/machine_learning.htm AI Horizon: Machine Learning, Part I: Types of Learning Problems]
* [http://www.aihorizon.com/essays/generalai/supervised_unsupervised_machine_learning.htm AI Horizon: Machine Learning, Part II: Supervised and Unsupervised Learning]
* [http://www.aihorizon.com/essays/generalai/no_free_lunch_machine_learning.htm AI Horizon: Machine Learning, Part III: Testing Algorithms, and The "No Free Lunch Theorem"]
==Chess==
* [http://www.top-5000.nl/authors/rebel/hints.htm Learning Methods] by [[Ed Schroder|Ed Schröder]]
* [http://archive.ics.uci.edu/ml/datasets/Chess+%28King-Rook+vs.+King-Pawn%29 UCI Machine Learning Repository: Chess (King-Rook vs. King-Pawn) Data Set] by [[Alen Shapiro]]
==Supervised Learning==
* [https://en.wikipedia.org/wiki/Supervised_learning Supervised learning from Wikipedia]
* [http://www.scholarpedia.org/article/Category:Supervised_learning Category: Supervised learning - Scholarpedia]
* [https://en.wikipedia.org/wiki/Boosting_%28machine_learning%29 Boosting (machine learning) from Wikipedia]
: [https://en.wikipedia.org/wiki/AdaBoost AdaBoost from Wikipedia]
* [https://en.wikipedia.org/wiki/Computational_learning_theory Computational learning theory from Wikipedia]
* [https://en.wikipedia.org/wiki/Support_vector_machine Support vector machine from Wikipedia]
==Unsupervised Learning==
* [https://en.wikipedia.org/wiki/Unsupervised_learning Unsupervised learning from Wikipedia]
* [http://www.scholarpedia.org/article/Category:Unsupervised_learning Category: Unsupervised learning - Scholarpedia]
==Reinforcement Learning==
* [https://en.wikipedia.org/wiki/Reinforcement_learning Reinforcement Learning from Wikipeadia]
* [http://www.incompleteideas.net/sutton/book/ebook/the-book.html Reinforcement Learning: An Introduction] ebook by [[Richard Sutton]] and [[Andrew Barto]]
* [http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching_files/games.pdf Reinforcement Learning in Classic Board Games] (pdf) by [[David Silver]]
* [http://www.scholarpedia.org/article/Category:Reinforcement_Learning Category: Reinforcement Learning - Scholarpedia]
* [http://www.scholarpedia.org/article/Reinforcement_learning Reinforcement Learning - Scholarpedia]
* [http://www.idsia.ch/%7Ejuergen/rl.html Reinforcement Learning and POMDPs] by [[Jürgen Schmidhuber]]
* [https://en.wikipedia.org/wiki/Q-learning Q-learning from Wikipeadia]
==TD Learning==
* [https://en.wikipedia.org/wiki/Temporal_difference_learning Temporal Difference Learning from Wikipeadia]
* [http://www.scholarpedia.org/article/Temporal_difference_learning Temporal difference learning - Scholarpedia]
* [https://en.wikipedia.org/wiki/TD-Gammon TD-Gammon from Wikipeadi]
* [http://www.scholarpedia.org/article/Td-gammon Td-gammon - Scholarpedia]
==Statistics==
* [https://en.wikipedia.org/wiki/Statistics Statistics from Wikipedia]
* [https://en.wikipedia.org/wiki/Statistical_learning_theory Statistical learning theory from Wikipedia]
* [https://en.wikipedia.org/wiki/Statistical_classification Statistical classification from Wikipedia]
: [https://en.wikipedia.org/wiki/Naive_Bayes_classifier Naive Bayes classifier from Wikipedia]
: [https://en.wikipedia.org/wiki/Probabilistic_classification Probabilistic classification from Wikipedia]
* [https://en.wikipedia.org/wiki/Statistical_mechanics Statistical mechanics from Wikipedia]
* [https://en.wikipedia.org/wiki/Bayesian_network Bayesian network from Wikipedia]
* [https://en.wikipedia.org/wiki/Bayesian_spam_filtering Bayesian spam filtering from Wikipedia]
* [http://dbacl.sourceforge.net/spam_chess-1.html Can a Bayesian spam filter play chess?] by [http://www.lbreyer.com/ Laird A. Breyer]
* [https://en.wikipedia.org/wiki/Computational_statistics Computational statistics from Wikipedia]
* [https://en.wikipedia.org/wiki/Data_clustering Cluster analysis from Wikipedia]
* [https://en.wikipedia.org/wiki/Cross_entropy Cross entropy from Wikipedia]
* [https://en.wikipedia.org/wiki/Dimensionality_reduction Dimensionality reduction from Wikipedia]
* [https://en.wikipedia.org/wiki/Feature_extraction Feature extraction from Wikipedia]
* [https://en.wikipedia.org/wiki/Feature_selection Feature selection from Wikipedia]
* [https://en.wikipedia.org/wiki/Mean_squared_error Mean squared error from Wikipedia]
* [https://en.wikipedia.org/wiki/Regression_analysis Regression analysis from Wikipedia]
: [https://en.wikipedia.org/wiki/Outline_of_regression_analysis Outline of regression analysis from Wikipedia]
: [https://en.wikipedia.org/wiki/Linear_regression Linear regression from Wikipedia]
: [https://en.wikipedia.org/wiki/Logistic_regression Logistic regression from Wikipedia]
* [https://en.wikipedia.org/wiki/Probability Probability from Wikipedia]
* [https://en.wikipedia.org/wiki/Probability_theory Probability theory from Wikipedia]
* [https://en.wikipedia.org/wiki/Probability_density_function Probability density function from Wikipedia]
* [https://en.wikipedia.org/wiki/Probability_distribution Probability distribution from Wikipedia]
: [https://en.wikipedia.org/wiki/Normal_distribution Normal distribution from Wikipedia]
* [https://en.wikipedia.org/wiki/Probability_measure Probability measure from Wikipedia]
* [https://en.wikipedia.org/wiki/Probability_space Probability space from Wikipedia]
* [https://en.wikipedia.org/wiki/Pseudorandomness Pseudorandomness from Wikipedia]
: [https://en.wikipedia.org/wiki/Pseudorandom_number_generator Pseudorandom number generator from Wikipedia]
: [https://en.wikipedia.org/wiki/Pseudo-random_number_sampling Pseudo-random number sampling from Wikipedia]
* [https://en.wikipedia.org/wiki/Randomness Randomness from Wikipedia]
: [https://en.wikipedia.org/wiki/Statistical_randomness Statistical randomness from Wikipedia]
* [https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_theory Vapnik–Chervonenkis theory from Wikipedia]
* [https://en.wikipedia.org/wiki/VC_dimension VC dimension from Wikipedia]
==Markov Models==
* [https://en.wikipedia.org/wiki/Markov_model Markov model from Wikipedia]
* [https://en.wikipedia.org/wiki/Markov_chain Markov chain from Wikipedia]
* [https://en.wikipedia.org/wiki/Hidden_Markov_model Hidden Markov model from Wikipedia]
* [https://en.wikipedia.org/wiki/Markov_decision_process Markov decision process (MDP) from Wikipedia]
* [https://en.wikipedia.org/wiki/Partially_observable_Markov_decision_process Partially observable Markov decision process (POMDP) from Wikipedia]
==NNs==
* [http://nic.schraudolph.org/teach/NNcourse/ Introduction to Neural Networks] by [[Nicol N. Schraudolph]] and [http://scholar.google.com/citations?user=E-vg2zQAAAAJ&hl=en Fred Cummins]
* [https://en.wikipedia.org/wiki/Biological_neural_network Biological neural network from Wikipedia]
* [https://www.scholarpedia.org/article/Category:Neural_networks Category: Neural networks - Scholarpedia]
* [https://en.wikipedia.org/wiki/Computational_neuroscience Computational neuroscience from Wikipedia]
* [https://en.wikipedia.org/wiki/Dendrite Dendrite from Wikipedia]
* [https://en.wikipedia.org/wiki/Hebbian_theory Hebbian theory from Wikipedia]
* [https://en.wikipedia.org/wiki/Generalized_Hebbian_Algorithm Generalized Hebbian Algorithm from Wikipedia]
* [https://en.wikipedia.org/wiki/Long-term_potentiation Long-term potentiation from Wikipedia]
* [https://en.wikipedia.org/wiki/Neuron Neuron from Wikipedia]
* [https://en.wikipedia.org/wiki/Neural_pathway Neural pathway from Wikipedia]
* [https://en.wikipedia.org/wiki/Neurotransmitter Neurotransmitter from Wikipedia]
* [https://en.wikipedia.org/wiki/Synapse Synapse from Wikipedia]
* [https://en.wikipedia.org/wiki/Synaptic_plasticity Synaptic plasticity from Wikipedia]
==ANNs==
* [https://en.wikipedia.org/wiki/Artificial_neural_network Artificial neural network from Wikipedia]
* [https://en.wikibooks.org/wiki/Artificial_Neural_Networks Artificial Neural Networks - Wikibooks]
* [http://de.slideshare.net/piuprabhu/chess-end-games-using-neural-networks-presentation Chess end games using Neural Networks]
'''Topics'''
* [https://en.wikipedia.org/wiki/Artificial_neuron Artificial neuron from Wikipedia]
* [https://en.wikipedia.org/wiki/Backpropagation Backpropagation from Wikipedia]
* [https://en.wikipedia.org/wiki/Connectionism Connectionism from Wikipedia]
* [https://en.wikipedia.org/wiki/Convolutional_neural_network Convolutional neural network from Wikipedia]
* [https://en.wikipedia.org/wiki/Feedforward_neural_network Feedforward neural network from Wikipedia]
* [http://www.scholarpedia.org/article/Fuzzy_neural_network Fuzzy neural network - Scholarpedia]
* [https://en.wikipedia.org/wiki/Multilayer_perceptron Multilayer perceptron from Wikipedia]
* [https://en.wikipedia.org/wiki/Neocognitron Neocognitron from Wikipedia]
* [https://en.wikipedia.org/wiki/Perceptron Perceptron from Wikipedia]
* [https://en.wikipedia.org/wiki/Recursive_neural_network Recursive neural network from Wikipedia]
* [https://en.wikipedia.org/wiki/Rprop Rprop from Wikipedia]
* [https://en.wikipedia.org/wiki/Time_delay_neural_network Time delay neural network from Wikipedia]
'''RNNs'''
* [https://en.wikipedia.org/wiki/Recurrent_neural_network Recurrent neural network from Wikipedia]
* [http://www.scholarpedia.org/article/Recurrent_neural_networks Recurrent neural networks - Scholarpedia]
* [http://people.idsia.ch/~juergen/rnn.html Recurrent Neural Networks] by [[Jürgen Schmidhuber]]
* [https://en.wikipedia.org/wiki/Boltzmann_machine Boltzmann machine from Wikipedia]
* <span id="Deep"></span>[https://en.wikipedia.org/wiki/Deep_learning Deep Learning from Wikipeadia]
* [https://en.wikipedia.org/wiki/Echo_state_network Echo state network]
* [https://en.wikipedia.org/wiki/Hopfield_network Hopfield network from Wikipedia]
* [http://www.scholarpedia.org/article/Hopfield_network Hopfield network - Scholarpedia]
* [https://en.wikipedia.org/wiki/Long_short_term_memory Long short term memory from Wikipedia]
'''Blogs'''
* [https://theneural.wordpress.com/ Neural Networks Blog] by [[Ilya Sutskever]]
* [http://dynamicnotions.blogspot.com/ Dynamic Notions] by [http://www.blogger.com/profile/07894297206547597169 John Wakefield] , a Blog about the evolution of neural networks with [[C sharp|C#]] samples:
: [http://dynamicnotions.blogspot.com/2008/09/single-layer-perceptron.html The Single Layer Perceptron]
: [http://dynamicnotions.blogspot.com/2008/09/hidden-neurons-and-feature-space.html Hidden Neurons and Feature Space]
: [http://dynamicnotions.blogspot.com/2008/09/training-neural-networks-using-back.html Training Neural Networks Using Back Propagation in C#]
: [http://dynamicnotions.blogspot.com/2008/09/data-mining-with-artificial-neural.html Data Mining with Artificial Neural Networks (ANN)]
* [http://www.welchlabs.com/blog Blog - Welch Labs]
==Courses==
* [http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html Advanced Topics: RL] by [[David Silver]]
* [http://work.caltech.edu/telecourse.html Learning From Data - Online Course (MOOC)] by [https://en.wikipedia.org/wiki/Yaser_Abu-Mostafa Yaser Abu-Mostafa], [https://en.wikipedia.org/wiki/California_Institute_of_Technology Caltech]
* [http://www.cedar.buffalo.edu/~srihari/CSE574/index.html Machine Learning and Probabilistic Graphical Models: Course Materials] by [https://en.wikipedia.org/wiki/Sargur_Srihari Sargur Srihari], [https://en.wikipedia.org/wiki/University_at_Buffalo University at Buffalo]
* [http://nbviewer.ipython.org/github/stephencwelch/Neural-Networks-Demysitifed/tree/master/ Neural Networks Demystified] by [https://twitter.com/stephencwelch Stephen Welch], [http://www.welchlabs.com/ Welch Labs]
* [http://www.holehouse.org/mlclass/index.html Stanford Machine Learning] by [[Andrew Ng]]
* [https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLA89DCFA6ADACE599&index=1 Lecture 1] | [https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 Machine Learning (Stanford)] by [[Andrew Ng]], [https://en.wikipedia.org/wiki/YouTube YouTube] Video
: {{#evu:https://www.youtube.com/watch?v=UzxYlbK2c7E|alignment=left|valignment=top}}

=References=
<references />

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