Michael L. Littman

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Michael L. Littman

Michael Lederman Littman,
an American mathematician, computer scientist and professor of CS at Brown University, and before at Rutgers University and Duke University. He received his Ph.D. in 1996 at Brown University on Algorithms for Sequential Decision Making under advisor Leslie P. Kaelbling . His research interests focus on stochastic games and reinforcement learning along with the related Markov Models for decision making, such as Markov Chain, Hidden Markov Model (HMM), Markov Decision Process (MDP), and Partially Observable Markov Decision Process (POMDP).

Crossword Solver

During his time at Duke University in the late 90s, Michael L. Littman worked on an automated crossword solver Proverb , which won an Outstanding Paper Award in 1999 from AAAI and competed in the American Crossword Puzzle Tournament .

See also

• Dr.Fill by Matthew L. Ginsberg
• FSSS-Minimax

Markov Models

Michael Littman's explanatory grid on Markov Models  :

Markov Models		Do we have control over the state transitons?
		NO	YES
Are the states completely observable?	YES	Markov Chain	MDP
	NO	HMM	POMDP

Selected Publications

1990 ...

• Justin A. Boyan, Michael L. Littman (1993). Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach. NIPS 1993, pdf
• Michael L. Littman (1994). Markov Games as a Framework for Multi-Agent Reinforcement Learning. International Conference on Machine Learning, pdf
• Michael L. Littman (1996). Algorithms for Sequential Decision Making . Ph.D. thesis, Brown University, pdf
• Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore (1996). Reinforcement Learning: A Survey. JAIR, Vol. 4, pdf
• Michael L. Littman (1997). Probabilistic Propositional Planning: Representations and Complexity. AAAI-1997
• Michael L. Littman, Judy Goldsmith, Martin Mundhenk (1998). The Computational Complexity of Probabilistic Planning. JAIR, Vol. 9, pdf
• Michael L. Littman (1999). Initial Experiments in Stochastic Satisfiability. [[Conferences#AAAI-99|AAAI-1999]
• Michael L. Littman, Greg A. Keim, Noam M. Shazeer (1999). Solving Crosswords with PROVERB. AAAI-1999
• Noam M. Shazeer, Michael L. Littman, Greg A. Keim (1999). Solving Crossword Puzzles as Probabilistic Constraint Satisfaction. AAAI-1999, CiteSeerX
• Greg A. Keim, Noam M. Shazeer, Michael L. Littman, Sushant Agarwal, Catherine M. Cheves, Joseph Fitzgerald, Jason Grosland, Fan Jiang, Shannon Pollard, Karl Weinmeister (1999). PROVERB: The Probabilistic Cruciverbalist. AAAI-1999, pdf

2000 ...

• Sebastian Thrun, Michael L. Littman (2000). A Review of Reinforcement Learning. AI Magazine, Vol. 21, No. 1, pdf
• Michael L. Littman (2000). Review: Computer Language Games. CG 2000
• Jiefu Shi, Michael L. Littman (2000). Abstraction Methods for Game Theoretic Poker. CG 2000
• Michael L. Littman, Richard Sutton, Satinder Singh (2001). Predictive Representations of State. NIPS 2001, pdf
• Michael L. Littman (2001). Value-function reinforcement learning in Markov games. Cognitive Systems Research, Vol. 2, pdf
• Michael L. Littman, Greg A. Keim, Noam M. Shazeer (2002). A probabilistic approach to solving crossword puzzles. Artificial Intelligence, Vol. 134, pdf
• Peter Stone, Robert Schapire, Michael L. Littman, János A. Csirik, David McAllester (2003). Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. JAIR, Vol. 19, pdf
• Michael L. Littman (2008). Autonomous Model Learning for Reinforcement Learning. QEST 2008

2010 ...

• Thomas J. Walsh, Sergiu Goschin, Michael L. Littman (2010). Integrating sample-based planning and model-based reinforcement learning. AAAI, pdf » MCTS, UCT, Reinforcement Learning
• Thomas J. Walsh, István Szita, Carlos Diuk, Michael L. Littman (2012). Exploring compact reinforcement-learning representations with linear regression. arXiv:1205.2606
• Michael L. Littman, Nishkam Ravi, Arjun Talwar, Martin Zinkevich (2012). An Efficient Optimal-Equilibrium Algorithm for Two-player Game Trees. arXiv:1206.6855
• Michael L. Littman (2012). Technical Perspective: A New Way to Search Game Trees. Communications of the ACM, Vol. 55, No. 3
• Ari Weinstein, Michael L. Littman (2012). Bandit-Based Planning and Learning in Continuous-Action Markov Decision Processes. ICAPS 2012
• Michael Kearns, Michael L. Littman, Satinder Singh (2013). Graphical Models for Game Theory. arXiv:1301.2281
• Anthony R. Cassandra, Michael L. Littman, Nevin L. Zhang (2013). Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes. arXiv:1302.1525
• Ari Weinstein, Michael L. Littman, Sergiu Goschin (2013). Rollout-based Game-tree Search Outprunes Traditional Alpha-beta. PMLR, Vol. 24 » MCTS, UCT
• Sergiu Goschin, Ari Weinstein, Michael L. Littman (2013). The Cross-Entropy Method Optimizes for Quantiles. ICML 2013
• Ari Weinstein, Michael L. Littman (2013). Open-Loop Planning in Large-Scale Stochastic Domains. AAAI-2013
• Kavosh Asadi, Michael L. Littman (2016). An Alternative Softmax Operator for Reinforcement Learning. arXiv:1612.05628
• Lucas Lehnert, Michael L. Littman (2018). Transfer with Model Features in Reinforcement Learning. arXiv:1807.01736

External Links

• Michael Littman's Home Page
• Michael L. Littman from Wikipedia
• The Mathematics Genealogy Project - Michael Littman

References

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