Difference between revisions of "Learning"

From Chessprogramming wiki
Jump to: navigation, search
(20 intermediate revisions by the same user not shown)
Line 10: Line 10:
  
 
=Learning Paradigms=  
 
=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.
+
There are three major learning [https://en.wikipedia.org/wiki/Paradigm paradigms], each corresponding to a particular abstract learning task. These are [[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==  
 +
''see main page [[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> .
 
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> .
  
Line 40: Line 42:
 
<span id="Programs"></span>
 
<span id="Programs"></span>
 
=Programs=
 
=Programs=
 +
* [[Allie]]
 
* [[AlphaZero]]
 
* [[AlphaZero]]
 
* [[Alexs]]
 
* [[Alexs]]
Line 82: Line 85:
 
==1950 ...==  
 
==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  
 
* [[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
+
: [[Claude Shannon]], [[John McCarthy]] (eds.) ('''1956'''). ''Automata Studies''. [https://www.wikidata.org/wiki/Q15763006 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] ('''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
 
* [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
Line 89: Line 92:
 
* [[Marvin Minsky]] ('''1954'''). ''Neural Nets and the Brain Model Problem''. Ph.D. dissertation, [https://en.wikipedia.org/wiki/Princeton_University Princeton University]
 
* [[Marvin Minsky]] ('''1954'''). ''Neural Nets and the Brain Model Problem''. Ph.D. dissertation, [https://en.wikipedia.org/wiki/Princeton_University Princeton University]
 
==1955 ...==
 
==1955 ...==
 +
* [[Mathematician#RRBush|Robert R. Bush]],  [[Mathematician#Mosteller|Frederick  Mosteller]] ('''1955'''). ''[https://psycnet.apa.org/record/1956-02349-000 Stochastic models for learning]''. [https://en.wikipedia.org/wiki/Wiley_(publisher) John Wiley & Sons]
 
* [[John von Neumann]] ('''1956'''). ''Probabilistic Logic and the Synthesis of Reliable Organisms From Unreliable Components''. in  
 
* [[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]
 
: [[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]
 +
* [[Mathematician#Mosteller|Frederick  Mosteller]] ('''1956'''). ''Stochastic Learning Models''. in [[Mathematician#JNeyman|Jerzy Neyman]] ('''1956'''). ''[https://projecteuclid.org/euclid.bsmsp/1200511851 Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Volume 5: Contributions to Econometrics, Industrial Research, and Psychometry]'', [https://pdfs.semanticscholar.org/4df2/038d7edee92e9fbd302b42cccee7b28f96ce.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>
 
* [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>
 
* [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>
Line 123: Line 128:
 
* [[Jacques Pitrat]] ('''1976'''). ''Realization of a Program Learning to Find Combinations at Chess.'' Computer Oriented Learning Processes (ed. J. Simon). Noordhoff, Groningen, The Netherlands.
 
* [[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]
 
* [[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]
+
* [[Ryszard Michalski]], [[Pericles Negri]] ('''1977'''). ''An experiment on inductive learning in chess endgames''. [https://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
 
* [[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.  
 
* [[Richard Sutton]] ('''1978'''). ''Single channel theory: A neuronal theory of learning''. Brain Theory Newsletter 3, No. 3/4, pp. 72-75.  
Line 133: Line 138:
 
* [[Alen Shapiro]], [[Tim Niblett]] ('''1982'''). ''Automatic Induction of Classification Rules for Chess End game.'' [[Advances in Computer Chess 3]]
 
* [[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]]
 
* [[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]
+
* [[Ryszard Michalski]], [[Jaime Carbonell]], [[Tom Mitchell]] ('''1983'''). ''[https://link.springer.com/book/10.1007%2F978-3-662-12405-5 Machine Learning: An Artificial Intelligence Approach]''. [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
 
* [[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  
 
* [[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)  
 
* [[Alen Shapiro]] ('''1983'''). ''The Role of Structured Induction in Expert Systems''. [[University of Edinburgh]], Machine Intelligence Research Unit (Ph.D. thesis)  
Line 145: Line 150:
 
* [[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]
 
* [[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.
 
* [[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]
+
* [[Ryszard Michalski]], [[Jaime Carbonell]], [[Tom Mitchell]] ('''1985, 2014'''). ''[https://www.elsevier.com/books/machine-learning/michalski/978-0-08-051054-5?gclid=EAIaIQobChMItc_hsp_34AIVUeR3Ch2l9QcDEAYYASABEgKW4_D_BwEMachine Learning: An Artificial Intelligence Approach, Volume I]''. [https://en.wikipedia.org/wiki/Morgan_Kaufmann_Publishers Morgan Kaufmann]
* [[Igor Roizen]], [[Judea Pearl]] ('''1985'''). ''Learning Link Probabilities in Causal Trees.'' Proceedings of the Second Conference on Uncertainty in Artificial Intelligence
+
* [[Igor Roizen]], [[Judea Pearl]] ('''1985'''). ''Learning Link Probabilities in Causal Trees.'' [[Laveen Kanal#Uncertainty AI 1|Uncertainty in Artificial Intelligence 1]]
 
'''1986'''
 
'''1986'''
 
* [[Steven Skiena]] ('''1986'''). ''An Overview of Machine Learning in Chess.'' [[ICGA Journal#9_1|ICCA Journal, Vol. 9, No. 1]]
 
* [[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].
+
* [[Jens Christensen]], [[Richard Korf]] ('''1986'''). ''A Unified Theory of Heuristic Evaluation functions and Its Applications to Learning.'' Proceedings of the AAAI-86, [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]
+
* [[Ryszard Michalski]], [[Jaime Carbonell]], [[Tom Mitchell]] ('''1986'''). ''[https://dl.acm.org/citation.cfm?id=21934 Machine Learning: An Artificial Intelligence Approach, Volume II]''. [https://en.wikipedia.org/wiki/Morgan_Kaufmann_Publishers Morgan Kaufmann]
* [[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
+
* [[Tom Mitchell]], [[Jaime Carbonell]], [[Ryszard Michalski]] ('''1986'''). ''[https://link.springer.com/book/10.1007%2F978-1-4613-2279-5 Machine Learning: A Guide to Current Research]''. [https://en.wikipedia.org/wiki/Wolters_Kluwer 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
 
* [[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'''
 
'''1987'''
Line 180: Line 185:
 
* [[Bruce Abramson]] ('''1990'''). ''On Learning and Testing Evaluation Functions.'' Journal of Experimental and Theoretical Artificial Intelligence 2: 241-251.
 
* [[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]]
 
* [[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]
+
* [[Yves Kodratoff]], [[Ryszard Michalski]] ('''1990, 2014'''). ''[https://www.elsevier.com/books/machine-learning/kodratoff/978-0-08-051055-2 Machine Learning : An Artificial Intelligence Approach, Volume III]''. [https://en.wikipedia.org/wiki/Morgan_Kaufmann_Publishers Morgan Kaufmann]
 
* [[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]
 
* [[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'''
 
'''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]
 
* [[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.
+
* [[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]
 
* [[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]]
 
* [[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]]
 
* [[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]]
 
* [[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
+
* [[Steven Walczak]] ('''1991'''). ''Predicting Actions from Induction on Past Performance''. Proceedings of the 8th International Workshop on Machine Learning
 
* [[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
 
* [[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 IJCNN'91], [https://bi.snu.ac.kr/Publications/Conferences/International/IJCNN91.pdf pdf]
 
* [[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 IJCNN'91], [https://bi.snu.ac.kr/Publications/Conferences/International/IJCNN91.pdf pdf]
 
* [[Byoung-Tak Zhang]], [[Gerd Veenker]] ('''1991'''). ''Focused incremental learning for improved generalization with reduced training sets''. ICANN'91, [https://bi.snu.ac.kr/Publications/Conferences/International/ICANN'91.pdf pdf]
 
* [[Byoung-Tak Zhang]], [[Gerd Veenker]] ('''1991'''). ''Focused incremental learning for improved generalization with reduced training sets''. ICANN'91, [https://bi.snu.ac.kr/Publications/Conferences/International/ICANN'91.pdf pdf]
 +
* [[Stephen Muggleton]] ('''1991'''). ''[https://link.springer.com/article/10.1007/BF03037089 Inductive Logic Programming]''. [https://link.springer.com/journal/354/8/4/page/1 New Generation Computing], Vol. 8, No. 4, [http://www.doc.ic.ac.uk/%7Eshm/Papers/ilp.pdf pdf]
 
'''1992'''
 
'''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]
 
* [[Miroslav Kubat]] ('''1992'''). ''Introduction to Machine Learning''. [http://dblp.uni-trier.de/db/conf/ac/ai1992.html#Kubat92 Advanced Topics in Artificial Intelligence 1992]
Line 208: Line 214:
 
* [[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]
 
* [[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]
 
* [[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]
 +
* [[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1993'''). ''[https://papers.nips.cc/paper/820-temporal-difference-learning-of-position-evaluation-in-the-game-of-go Temporal Difference Learning of Position Evaluation in the Game of Go]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-6-1993 NIPS 1993]
 
'''1994'''
 
'''1994'''
 
* [[Eduardo F. Morales]] ('''1994'''). ''Learning Patterns for Playing Strategies''. [[ICGA Journal#17_1|ICCA Journal, Vol. 17, No. 1]]
 
* [[Eduardo F. Morales]] ('''1994'''). ''Learning Patterns for Playing Strategies''. [[ICGA Journal#17_1|ICCA Journal, Vol. 17, No. 1]]
Line 213: Line 220:
 
* [[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]] ('''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.
 
* [[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]
+
* [[Ryszard Michalski]], [[George Tecuci]] ('''1994'''). ''Machine Learning: A Multistrategy Approach, Volume IV''. [https://en.wikipedia.org/wiki/Morgan_Kaufmann_Publishers Morgan Kaufmann]
 
* [[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]
 
* [[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
 
* [[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]
 
* [[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 ...==  
 
==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
 
* [[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
Line 265: Line 271:
 
* [[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]
 
* [[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]
 
* [[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]
+
* [[Ryszard Michalski]], [[Ivan Bratko]], [[Miroslav Kubat]] (eds.) ('''1998'''). ''[https://www.wiley.com/en-gb/Machine+Learning+and+Data+Mining%3A+Methods+and+Applications-p-9780471971993 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]
 
: [[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>
 
* [[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>
Line 271: Line 277:
 
* [[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'''). ''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]
 
* [[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)
+
* [[Ryszard Michalski]] ('''1998'''). ''Learnable Evolution: Combining Symbolic and Evolutionary Learning''. Proceedings of the Fourth International Workshop on Multistrategy Learning
 
* [[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]
 
* [[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'''). ''Fast online Q (λ)''. [https://en.wikipedia.org/wiki/Machine_Learning_%28journal%29 Machine Learning], Vol. 33, No. 1
 
* [[Marco Wiering]],  [[Jürgen Schmidhuber]] ('''1998'''). ''Fast online Q (λ)''. [https://en.wikipedia.org/wiki/Machine_Learning_%28journal%29 Machine Learning], Vol. 33, No. 1
Line 303: Line 309:
 
* [[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]
 
* [[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'''
 
'''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]]
+
* [[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]''. [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#1993|1993 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]
 
* [[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]
 
* [[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]
Line 327: Line 333:
 
* [[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]
 
* [[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]
 
* [[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]
 +
* [[Ryan Rifkin]] ('''2002'''). ''Everything Old Is New Again: A Fresh Look at Historical Approaches to Machine Learning''. Ph.D thesis, [[Massachusetts Institute of Technology|MIT]], [http://cbcl.mit.edu/publications/theses/thesis-rifkin.pdf pdf]
 
'''2003'''
 
'''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]], [[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]
Line 344: Line 351:
 
* [[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]
 
* [[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]
 
* [[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]
+
* [[Marek Strejczek]] ('''2004'''). ''Some aspects of chess programming'', M.Sc. thesis, [[Technical University of Łódź]]
 
* [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>
 
* [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]
 
* [[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]
Line 384: Line 391:
 
* [[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]]
 
* [[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]''.
 
* [[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]''.
 +
* [[Martin Možina]], [https://dblp.uni-trier.de/pers/hd/z/Zabkar:Jure Jure Žabkar], [[Ivan Bratko]] ('''2007'''). ''Argument Based Machine Learning''. [https://en.wikipedia.org/wiki/Artificial_Intelligence_(journal) Artificial Intelligence], Vol. 171, Nos. 10-15, [https://ailab.si/martin/abml/ABCN2aij_final.pdf pdf preprint]
 
* [[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
 
* [[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]]
 
* [[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]]
Line 404: Line 412:
 
* [[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]
 
* [[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]
 
* [[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]
 +
* [[Balázs Csanád Csáji]], [https://dblp.dagstuhl.de/pers/hd/m/Monostori:L=aacute=szl=oacute= László Monostori] ('''2008, 2014'''). ''Adaptive stochastic resource control: a machine learning approach''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research Journal of Artificial Intelligence Research], Vol. 32, [https://arxiv.org/abs/1401.3434 arXiv:1401.3434]
 
'''2009'''
 
'''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]
 
* [[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]
Line 419: Line 428:
 
* [[Mark Levene]], [[Trevor Fenner]] ('''2009'''). ''A Methodology for Learning Players' Styles from Game Records''. [http://arxiv.org/abs/0904.2595v1 arXiv:0904.2595v1]
 
* [[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]
 
* [[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]
 +
* [https://dblp.uni-trier.de/pers/hd/h/Hall:Mark_A= Mark A. Hall], [https://dblp.uni-trier.de/pers/hd/f/Frank:Eibe Eibe Frank], [[Geoffrey Holmes]], [[Bernhard Pfahringer]], [https://dblp.uni-trier.de/pers/hd/r/Reutemann:Peter Peter Reutemann], [[Ian H. Witten]] ('''2009'''). ''The WEKA data mining software: an update''. [https://dblp.uni-trier.de/db/journals/sigkdd/sigkdd11.html SIGKDD Explorations], Vol. 11, No. 1, [https://www.kdd.org/exploration_files/p2V11n1.pdf pdf] <ref>[https://en.wikipedia.org/wiki/Weka_(machine_learning) Weka (machine learning) from Wikipedia]</ref>
 
==2010 ...==
 
==2010 ...==
 +
* [https://dblp.uni-trier.de/pers/hd/f/Frank:Eibe Eibe Frank], [https://dblp.uni-trier.de/pers/hd/h/Hall:Mark_A= Mark A. Hall], [[Geoffrey Holmes]], [https://dblp.uni-trier.de/pers/hd/k/Kirkby:Richard Richard Kirkby], [[Bernhard Pfahringer]], [[Ian H. Witten]], [https://dblp.uni-trier.de/pers/hd/t/Trigg:Leonard_E= Len Trigg]  ('''2010'''). ''[https://link.springer.com/chapter/10.1007/978-0-387-09823-4_66 Weka-A Machine Learning Workbench for Data Mining]''. [https://link.springer.com/book/10.1007/978-0-387-09823-4 Data Mining and Knowledge Discovery Handbook], [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
 
* [[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]
 
* [[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]
 
* [[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]
Line 484: Line 495:
 
* [[Hado van Hasselt]], [[Arthur Guez]], [[David Silver]] ('''2015'''). ''Deep Reinforcement Learning with Double Q-learning''. [http://arxiv.org/abs/1509.06461 arXiv:1509.06461]
 
* [[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]
 
* [[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]  
+
* [[Miroslav Kubat]] ('''2015'''). ''[https://www.springer.com/us/book/9783319348865#otherversion=9783319200095 An Introduction to Machine Learning]''. [https://en.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
 
* [[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'''
 
'''2016'''
Line 493: Line 504:
 
* [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]
 
* [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]
 
* [[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]
 +
* [[Ian H. Witten]], [https://dblp.uni-trier.de/pers/hd/f/Frank:Eibe Eibe Frank], [https://dblp.uni-trier.de/pers/hd/h/Hall:Mark_A= Mark A. Hall], [http://www.professeurs.polymtl.ca/christopher.pal/ Christopher Pal] ('''2016'''). ''[https://www.cs.waikato.ac.nz/~ml/weka/book.html Data Mining: Practical Machine Learning Tools and Techniques]''. 4th Edition, [https://en.wikipedia.org/wiki/Morgan_Kaufmann_Publishers Morgan Kaufmann]
 
'''2017'''
 
'''2017'''
 +
* [[Stephen Muggleton]] ('''2017'''). ''Meta-Interpretive Learning: Achievements and Challenges''. Invited Paper, [https://dblp.uni-trier.de/db/conf/ruleml/ruleml2017.html RuleML+RR 2017], [https://www.doc.ic.ac.uk/~shm/Papers/rulemlabs.pdf pdf]
 
* [[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]]
 
* [[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]
 
* [[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]]
 
* [[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]]
 +
* [[Miroslav Kubat]] ('''2017'''). ''[https://www.springer.com/gp/book/9783319639123 An Introduction to Machine Learning]''. Second Edition, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
 
'''2018'''
 
'''2018'''
 
* [[Arthur Guez]], [[Théophane Weber]], [[Ioannis Antonoglou]], [[Karen Simonyan]], [[Oriol Vinyals]], [[Daan Wierstra]], [[Rémi Munos]], [[David Silver]] ('''2018'''). ''Learning to Search with MCTSnets''. [https://arxiv.org/abs/1802.04697 arXiv:1802.04697] » [[Monte-Carlo Tree Search]]
 
* [[Arthur Guez]], [[Théophane Weber]], [[Ioannis Antonoglou]], [[Karen Simonyan]], [[Oriol Vinyals]], [[Daan Wierstra]], [[Rémi Munos]], [[David Silver]] ('''2018'''). ''Learning to Search with MCTSnets''. [https://arxiv.org/abs/1802.04697 arXiv:1802.04697] » [[Monte-Carlo Tree Search]]
 
* [[Matthia Sabatelli]], [[Francesco Bidoia]], [[Valeriu Codreanu]], [[Marco Wiering]] ('''2018'''). ''Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead''. ICPRAM 2018, [http://www.ai.rug.nl/~mwiering/GROUP/ARTICLES/ICPRAM_CHESS_DNN_2018.pdf pdf]
 
* [[Matthia Sabatelli]], [[Francesco Bidoia]], [[Valeriu Codreanu]], [[Marco Wiering]] ('''2018'''). ''Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead''. ICPRAM 2018, [http://www.ai.rug.nl/~mwiering/GROUP/ARTICLES/ICPRAM_CHESS_DNN_2018.pdf pdf]
 +
* [[Takeshi Ito]] ('''2018'''). ''Game learning support system based on future position''. [[CG 2018]], [[ICGA Journal#40_4|ICGA Journal, Vol. 40, No. 4]]
 +
'''2019'''
 +
* [[Herilalaina Rakotoarison]], [[Marc Schoenauer]], [[Michèle Sebag]] ('''2019'''). ''Automated Machine Learning with Monte-Carlo Tree Search''. [https://arxiv.org/abs/1906.00170 arXiv:1906.00170]
 +
* [[Frank Hutter]], [https://dblp.org/pers/hd/k/Kotthoff:Lars Lars Kotthoff], [https://dblp.org/pers/hd/v/Vanschoren:Joaquin Joaquin Vanschoren] (eds.) ('''2019'''). ''[https://link.springer.com/book/10.1007%2F978-3-030-05318-5 Automated Machine Learning]''. [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
  
 
=Forum Posts=
 
=Forum Posts=
 
==1998 ...==
 
==1998 ...==
 +
* [https://www.stmintz.com/ccc/index.php?id=16136 Opponent specific learning...] by [[Daniel Homan]], [[CCC]], March 26, 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=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=25754 BookLearning Under the Microscope!!!] by Robert Henry Durrett, [[CCC]], August 31, 1998
Line 539: Line 558:
 
* [https://en.wikipedia.org/wiki/List_of_machine_learning_concepts List of machine learning concepts from Wikipedia]
 
* [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/Apprenticeship_learning Apprenticeship learning from Wikipedia]
 +
* [https://en.wikipedia.org/wiki/Automated_machine_learning Automated machine learning from Wikipedia]
 +
* [https://en.wikipedia.org/wiki/Data_mining Data mining from Wikipeadia]
 
* [https://en.wikipedia.org/wiki/Ensemble_learning Ensemble 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/Explanation-based_learning Explanation-based learning from Wikipedia]
Line 684: Line 705:
 
=References=  
 
=References=  
 
<references />
 
<references />
 
 
'''[[Main Page|Up one Level]]'''
 
'''[[Main Page|Up one Level]]'''
 +
[[Category:Videos]]

Revision as of 10:42, 12 September 2019

Home * Learning

Learning [1]

Learning,
the process of acquiring new knowledge which involves synthesizing different types of information. 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 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 evolutionary computation and its sub-areas of genetic algorithms, and genetic programming, that mimics the process of natural evolution, as further mentioned in automated tuning. The process of learning often implies understanding, perception or reasoning. So called Rote learning avoids understanding and focuses on memorization. Inductive learning takes examples and generalizes rather than starting with existing knowledge. Deductive learning takes abstract concepts to make sense of examples [2].

Learning inside a Chess Program

Learning inside a chess program may address several disjoint issues. A persistent hash table remembers "important" positions from earlier games inside the search with its exact score [3]. Worse positions may be avoided in advance. 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 [4]. Another application is learning evaluation weights of various features, f. i. piece- [5] or piece-square [6] values or mobility. Programs may also learn to control search [7] or time usage [8].

Learning Paradigms

There are three major learning paradigms, each corresponding to a particular abstract learning task. These are supervised learning, unsupervised learning and reinforcement learning. Usually any given type of neural network architecture can be employed in any of those tasks.

Supervised Learning

see main page 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 [9] .

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 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. 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 Markov decision processes (MDPs) from the field of optimal control.

Learning Topics

Programs

See also

Selected Publications

[10]

1940 ...

1950 ...

Claude Shannon, John McCarthy (eds.) (1956). Automata Studies. Annals of Mathematics Studies, No. 34
Alan Turing, Jack Copeland (editor) (2004). The Essential Turing, Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life plus The Secrets of Enigma. Oxford University Press, amazon, google books

1955 ...

Claude Shannon, John McCarthy (eds.) (1956). Automata Studies. Annals of Mathematics Studies, No. 34, pdf

1960 ...

1965 ...

1970 ...

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. pdf
  • Ryszard Michalski, Pericles Negri (1977). An experiment on inductive learning in chess endgames. Machine Intelligence 8, 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 ...

1985 ...

1986

1987

1988

1989

1990 ...

1991

1992

1993

1994

1995 ...

1996

1997

1998

Miroslav Kubat, Ivan Bratko, Ryszard Michalski (1998). A Review of Machine Learning Methods. pdf

1999

2000 ...

2001

2002

2003

2004

2005 ...

2006

2007

2008

2009

2010 ...

2011

2012

István Szita (2012). Reinforcement Learning in Games. Chapter 17

2013

2014

2015 ...

2016

2017

2018

2019

Forum Posts

1998 ...

2000 ...

2005 ...

2010 ...

2015 ...

External Links

Machine Learning

AI

Learning I
Learning II

Chess

Supervised Learning

AdaBoost from Wikipedia

Unsupervised Learning

Reinforcement Learning

TD Learning

Statistics

Naive Bayes classifier from Wikipedia
Probabilistic classification from Wikipedia
Outline of regression analysis from Wikipedia
Linear regression from Wikipedia
Logistic regression from Wikipedia
Normal distribution from Wikipedia
Pseudorandom number generator from Wikipedia
Pseudo-random number sampling from Wikipedia
Statistical randomness from Wikipedia

Markov Models

NNs

ANNs

Topics

RNNs

Blogs

The Single Layer Perceptron
Hidden Neurons and Feature Space
Training Neural Networks Using Back Propagation in C#
Data Mining with Artificial Neural Networks (ANN)

Courses

References

  1. A depiction of the world's oldest continually operating university, the University of Bologna, Italy, by Laurentius de Voltolina, second half of 14th century, Learning from Wikipedia
  2. Inductive learning vs Deductive learning
  3. David Slate (1987). A Chess Program that uses its Transposition Table to Learn from Experience. ICCA Journal, Vol. 10, No. 2
  4. Robert Hyatt (1999). Book Learning - a Methodology to Tune an Opening Book Automatically. ICCA Journal, Vol. 22, No. 1
  5. Don Beal, Martin C. Smith (1997). Learning Piece Values Using Temporal Differences. ICCA Journal, Vol. 20, No. 3
  6. Don Beal, Martin C. Smith (1999). Learning Piece-Square Values using Temporal Differences. ICCA Journal, Vol. 22, No. 4
  7. Yngvi Björnsson, Tony Marsland (2001). Learning Search Control in Adversary Games. Advances in Computer Games 9, pdf
  8. Levente Kocsis, Jos Uiterwijk, Jaap van den Herik (2000). Learning Time Allocation using Neural Networks. CG 2000, postscript
  9. AI Horizon: Machine Learning, Part II: Supervised and Unsupervised Learning
  10. online papers from Machine Learning in Games by Jay Scott
  11. Rosenblatt's Contributions
  12. Ratio Club from Wikipedia
  13. Royal Radar Establishment from Wikipedia
  14. see Swap-off by Helmut Richter
  15. The abandonment of connectionism in 1969 - Wikipedia
  16. Frank Rosenblatt (1962). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books
  17. Long short term memory from Wikipedia
  18. Tsumego from Wikipedia
  19. Learnable Evolution Model from Wikipedia
  20. University of Bristol - Department of Computer Science - Technical Reports
  21. Generalized Hebbian Algorithm from Wikipedia
  22. Dap Hartmann (2010). Mimicking the Black Box - Genetically evolving evaluation functions and search algorithms. Review on Omid David's Ph.D. Thesis, ICGA Journal, Vol 33, No. 1
  23. Monte-Carlo Simulation Balancing - videolectures.net by David Silver
  24. MATLAB from Wikipedia
  25. Weka (machine learning) from Wikipedia
  26. Ms. Pac-Man from Wikipedia
  27. Demystifying Deep Reinforcement Learning by Tambet Matiisen, Nervana, December 21, 2015
  28. Patent US20150100530 - Methods and apparatus for reinforcement learning - Google Patents
  29. Jaap van den Herik wint Humies Award 2014 - LIACS - Leiden Institute of Advanced Computer Science
  30. 2048 (video game) from Wikipedia
  31. Teaching Deep Convolutional Neural Networks to Play Go by Hiroshi Yamashita, The Computer-go Archives, December 14, 2014
  32. Teaching Deep Convolutional Neural Networks to Play Go by Michel Van den Bergh, CCC, December 16, 2014
  33. Convolutional neural network from Wikipedia
  34. Best Paper Awards | TAAI 2014
  35. DeepChess: Another deep-learning based chess program by Matthew Lai, CCC, October 17, 2016
  36. ICANN 2016 | Recipients of the best paper awards
  37. Using GAN to play chess by Evgeniy Zheltonozhskiy, CCC, February 23, 2017
  38. Naive Bayes classifier from Wikipedia
  39. Amir Ban (2012). Automatic Learning of Evaluation, with Applications to Computer Chess. Discussion Paper 613, The Hebrew University of Jerusalem - Center for the Study of Rationality, Givat Ram
  40. Christopher Clark, Amos Storkey (2014). Teaching Deep Convolutional Neural Networks to Play Go. arXiv:1412.3409
  41. Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver (2014). Move Evaluation in Go Using Deep Convolutional Neural Networks. arXiv:1412.6564v1

Up one Level