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Michael L. Littman
Author information
- affiliation: Brown University, Department of Computer Science
- affiliation: Rutgers University, Department of Electrical and Computer Engineering
2010 – today
- 2013
[c104]- [c103]
- [e2]Marie desJardins, Michael L. Littman (Eds.): Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, July 14-18, 2012, Bellevue, Washington, USA. AAAI Press 2013, ISBN 978-1-57735-615-8
- [i20]Michael Kearns, Michael L. Littman, Satinder P. Singh: Graphical Models for Game Theory. CoRR abs/1301.2281 (2013)
- [i19]Anthony R. Cassandra, Michael L. Littman, Nevin Lianwen Zhang: Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes. CoRR abs/1302.1525 (2013)
- [i18]Judy Goldsmith, Michael L. Littman, Martin Mundhenk: The Complexity of Plan Existence and Evaluation in Probabilistic Domains. CoRR abs/1302.1540 (2013)
- [i17]Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling: On the Complexity of Solving Markov Decision Problems. CoRR abs/1302.4971 (2013)
- 2012
- [j40]Michael L. Littman: A new way to search game trees: technical perspective. Commun. ACM 55(3): 105 (2012)
- [j39]Michael L. Littman: Inducing Partially Observable Markov Decision Processes. Journal of Machine Learning Research - Proceedings Track 21: 145-148 (2012)
- [j38]Nikos Vlassis, Michael L. Littman, David Barber: On the Computational Complexity of Stochastic Controller Optimization in POMDPs. TOCT 4(4): 12 (2012)
- [j37]Thomas J. Walsh, Michael L. Littman, Alexander Borgida: Learning web-service task descriptions from traces. Web Intelligence and Agent Systems 10(4): 397-421 (2012)
- [c102]Zongzhang Zhang, Michael L. Littman, Xiaoping Chen: Covering Number as a Complexity Measure for POMDP Planning and Learning. AAAI 2012
- [c101]Michael Wunder, John Robert Yaros, Michael Kaisers, Michael L. Littman: A framework for modeling population strategies by depth of reasoning. AAMAS 2012: 947-954
- [c100]Ari Weinstein, Michael L. Littman: Bandit-Based Planning and Learning in Continuous-Action Markov Decision Processes. ICAPS 2012
- [i16]John Asmuth, Michael L. Littman: Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search. CoRR abs/1202.3699 (2012)
- [i15]Thomas J. Walsh, Istvan Szita, Carlos Diuk, Michael L. Littman: Exploring compact reinforcement-learning representations with linear regression. CoRR abs/1205.2606 (2012)
- [i14]John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate: A Bayesian Sampling Approach to Exploration in Reinforcement Learning. CoRR abs/1205.2664 (2012)
- [i13]Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy: CORL: A Continuous-state Offset-dynamics Reinforcement Learner. CoRR abs/1206.3231 (2012)
- [i12]Enrique Munoz de Cote, Michael L. Littman: A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games. CoRR abs/1206.3277 (2012)
- 2011
- [j36]Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins: Democratic approximation of lexicographic preference models. Artif. Intell. 175(7-8): 1290-1307 (2011)
- [j35]Changhe Yuan, Heejin Lim, Michael L. Littman: Most Relevant Explanation: computational complexity and approximation methods. Ann. Math. Artif. Intell. 61(3): 159-183 (2011)
- [j34]Brian Russell, Michael L. Littman, Wade Trappe: Integrating machine learning in ad hoc routing: A wireless adaptive routing protocol. Int. J. Communication Systems 24(7): 950-966 (2011)
- [j33]Lihong Li, Michael L. Littman, Thomas J. Walsh, Alexander L. Strehl: Knows what it knows: a framework for self-aware learning. Machine Learning 82(3): 399-443 (2011)
- [j32]Shimon Whiteson, Michael L. Littman: Introduction to the special issue on empirical evaluations in reinforcement learning. Machine Learning 84(1-2): 1-6 (2011)
- [j31]
- [c99]Christopher R. Mansley, Ari Weinstein, Michael L. Littman: Sample-Based Planning for Continuous Action Markov Decision Processes. ICAPS 2011
- [c98]Michael Wunder, Michael Kaisers, John Robert Yaros, Michael L. Littman: Using iterated reasoning to predict opponent strategies. AAMAS 2011: 593-600
- [c97]Sergiu Goschin, Michael L. Littman, David H. Ackley: The effects of selection on noisy fitness optimization. GECCO 2011: 2059-2066
- [c96]Jordan Ash, Monica Babes, Gal Cohen, Sameen Jalal, Sam Lichtenberg, Michael L. Littman, Vukosi N. Marivate, Phillip Quiza, Blase Ur, Emily Zhang: Scratchable Devices: User-Friendly Programming for Household Appliances. HCI (3) 2011: 137-146
- [c95]Monica Babes, Vukosi N. Marivate, Kaushik Subramanian, Michael L. Littman: Apprenticeship Learning About Multiple Intentions. ICML 2011: 897-904
- [c94]John Asmuth, Michael L. Littman: Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search. UAI 2011: 19-26
- [i11]Michael J. Kearns, Michael L. Littman, Satinder P. Singh, Peter Stone: ATTac-2000: An Adaptive Autonomous Bidding Agent. CoRR abs/1106.0678 (2011)
- [i10]János A. Csirik, Michael L. Littman, David A. McAllester, Robert E. Schapire, Peter Stone: Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. CoRR abs/1106.5270 (2011)
- [i9]Nikos Vlassis, Michael L. Littman, David Barber: On the computational complexity of stochastic controller optimization in POMDPs. CoRR abs/1107.3090 (2011)
- 2010
- [j30]Lihong Li, Michael L. Littman: Reducing reinforcement learning to KWIK online regression. Ann. Math. Artif. Intell. 58(3-4): 217-237 (2010)
- [j29]Ali Nouri, Michael L. Littman: Dimension reduction and its application to model-based exploration in continuous spaces. Machine Learning 81(1): 85-98 (2010)
- [c93]Kaushik Subramanian, Michael L. Littman: Efficient Apprenticeship Learning with Smart Humans. Enabling Intelligence through Middleware 2010
- [c92]Thomas J. Walsh, Sergiu Goschin, Michael L. Littman: Integrating Sample-Based Planning and Model-Based Reinforcement Learning. AAAI 2010
- [c91]Michael Wunder, Michael L. Littman, Michael Kaisers, John Robert Yaros: A Cognitive Hierarchy Model Applied to the Lemonade Game. Interactive Decision Theory and Game Theory 2010
- [c90]Thomas J. Walsh, Kaushik Subramanian, Michael L. Littman, Carlos Diuk: Generalizing Apprenticeship Learning across Hypothesis Classes. ICML 2010: 1119-1126
- [c89]Michael Wunder, Michael L. Littman, Monica Babes: Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration. ICML 2010: 1167-1174
- [c88]Marie desJardins, Michael L. Littman: Broadening student enthusiasm for computer science with a great insights course. SIGCSE 2010: 157-161
2000 – 2009
- 2009
- [j28]Thomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman: Learning and planning in environments with delayed feedback. Autonomous Agents and Multi-Agent Systems 18(1): 83-105 (2009)
- [j27]Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy: Provably Efficient Learning with Typed Parametric Models. Journal of Machine Learning Research 10: 1955-1988 (2009)
- [j26]Alexander L. Strehl, Lihong Li, Michael L. Littman: Reinforcement Learning in Finite MDPs: PAC Analysis. Journal of Machine Learning Research 10: 2413-2444 (2009)
- [c87]Lihong Li, Michael L. Littman, Christopher R. Mansley: Online exploration in least-squares policy iteration. AAMAS (2) 2009: 733-739
- [c86]John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate: A Bayesian Sampling Approach to Exploration in Reinforcement Learning. UAI 2009: 19-26
- [c85]Thomas J. Walsh, Istvan Szita, Carlos Diuk, Michael L. Littman: Exploring compact reinforcement-learning representations with linear regression. UAI 2009: 591-598
- [e1]Andrea Pohoreckyj Danyluk, Léon Bottou, Michael L. Littman (Eds.): Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009. ACM International Conference Proceeding Series 382, ACM 2009, ISBN 978-1-60558-516-1
- [r1]Carlos Diuk, Michael L. Littman: Hierarchical Reinforcement Learning. Encyclopedia of Artificial Intelligence 2009: 825-830
- 2008
- [j25]David L. Roberts, Charles L. Isbell, Michael L. Littman: Optimization problems involving collections of dependent objects. Annals OR 163(1): 255-270 (2008)
- [j24]Alexander L. Strehl, Michael L. Littman: An analysis of model-based Interval Estimation for Markov Decision Processes. J. Comput. Syst. Sci. 74(8): 1309-1331 (2008)
- [c84]John Asmuth, Michael L. Littman, Robert Zinkov: Potential-based Shaping in Model-based Reinforcement Learning. AAAI 2008: 604-609
- [c83]Thomas J. Walsh, Michael L. Littman: Efficient Learning of Action Schemas and Web-Service Descriptions. AAAI 2008: 714-719
- [c82]Monica Babes, Enrique Munoz de Cote, Michael L. Littman: Social reward shaping in the prisoner's dilemma. AAMAS (3) 2008: 1389-1392
- [c81]Carlos Diuk, Andre Cohen, Michael L. Littman: An object-oriented representation for efficient reinforcement learning. ICML 2008: 240-247
- [c80]Lihong Li, Michael L. Littman, Thomas J. Walsh: Knows what it knows: a framework for self-aware learning. ICML 2008: 568-575
- [c79]Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman: An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning. ICML 2008: 752-759
- [c78]Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins: Democratic approximation of lexicographic preference models. ICML 2008: 1200-1207
- [c77]Lihong Li, Michael L. Littman: Efficient Value-Function Approximation via Online Linear Regression. ISAIM 2008
- [c76]
- [c75]
- [c74]Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy: CORL: A Continuous-state Offset-dynamics Reinforcement Learner. UAI 2008: 53-61
- [c73]Enrique Munoz de Cote, Michael L. Littman: A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games. UAI 2008: 419-426
- 2007
- [j23]Martin Zinkevich, Amy Greenwald, Michael L. Littman: A hierarchy of prescriptive goals for multiagent learning. Artif. Intell. 171(7): 440-447 (2007)
- [j22]Amy Greenwald, Michael L. Littman: Introduction to the special issue on learning and computational game theory. Machine Learning 67(1-2): 3-6 (2007)
- [c72]Bethany R. Leffler, Michael L. Littman, Timothy Edmunds: Efficient Reinforcement Learning with Relocatable Action Models. AAAI 2007: 572-577
- [c71]Alexander L. Strehl, Carlos Diuk, Michael L. Littman: Efficient Structure Learning in Factored-State MDPs. AAAI 2007: 645-650
- [c70]Thomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman: Planning and Learning in Environments with Delayed Feedback. ECML 2007: 442-453
- [c69]Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman: Analyzing feature generation for value-function approximation. ICML 2007: 737-744
- [c68]Alexander L. Strehl, Michael L. Littman: Online Linear Regression and Its Application to Model-Based Reinforcement Learning. NIPS 2007
- 2006
- [c67]David L. Roberts, Mark J. Nelson, Charles Lee Isbell Jr., Michael Mateas, Michael L. Littman: Targeting Specific Distributions of Trajectories in MDPs. AAAI 2006: 1213-1218
- [c66]Carlos Diuk, Michael L. Littman: A Change Detection Model for Non-Stationary k-Armed Bandit Problems. AAAI Spring Symposium: Between a Rock and a Hard Place: Cognitive Science Principles Meet AI-Hard Problems 2006: 39
- [c65]Carlos Diuk, Alexander L. Strehl, Michael L. Littman: A hierarchical approach to efficient reinforcement learning in deterministic domains. AAMAS 2006: 313-319
- [c64]Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman: PAC model-free reinforcement learning. ICML 2006: 881-888
- [c63]Alexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh: Experience-efficient learning in associative bandit problems. ICML 2006: 889-896
- [c62]Lihong Li, Thomas J. Walsh, Michael L. Littman: Towards a Unified Theory of State Abstraction for MDPs. ISAIM 2006
- [c61]Michael L. Littman, Nishkam Ravi, Arjun Talwar, Martin Zinkevich: An Efficient Optimal-Equilibrium Algorithm for Two-player Game Trees. UAI 2006
- [c60]Alexander L. Strehl, Lihong Li, Michael L. Littman: Incremental Model-based Learners With Formal Learning-Time Guarantees. UAI 2006
- 2005
- [j21]Nicholas L. Cassimatis, Sean Luke, Simon D. Levy, Ross W. Gayler, Pentti Kanerva, Chris Eliasmith, Timothy W. Bickmore, Alan C. Schultz, Randall Davis, James A. Landay, Robert C. Miller, Eric Saund, Thomas F. Stahovich, Michael L. Littman, Satinder P. Singh, Shlomo Argamon, Shlomo Dubnov: Reports on the 2004 AAAI Fall Symposia. AI Magazine 26(1): 98-102 (2005)
- [j20]Michael L. Littman, Peter Stone: A polynomial-time Nash equilibrium algorithm for repeated games. Decision Support Systems 39(1): 55-66 (2005)
- [j19]Håkan L. S. Younes, Michael L. Littman, David Weissman, John Asmuth: The First Probabilistic Track of the International Planning Competition. J. Artif. Intell. Res. (JAIR) 24: 851-887 (2005)
- [j18]Peter D. Turney, Michael L. Littman: Corpus-based Learning of Analogies and Semantic Relations. Machine Learning 60(1-3): 251-278 (2005)
- [c59]
- [c58]Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, Michael L. Littman: Activity Recognition from Accelerometer Data. AAAI 2005: 1541-1546
- [c57]Alexander L. Strehl, Michael L. Littman: A theoretical analysis of Model-Based Interval Estimation. ICML 2005: 856-863
- [c56]
- [c55]Bethany R. Leffler, Michael L. Littman, Alexander L. Strehl, Thomas J. Walsh: Efficient Exploration With Latent Structure. Robotics: Science and Systems 2005: 81-88
- [i8]Peter D. Turney, Michael L. Littman, Jeffrey Bigham, Victor Shnayder: Combining Independent Modules in Lexical Multiple-Choice Problems. CoRR abs/cs/0501018 (2005)
- [i7]Peter D. Turney, Michael L. Littman: Corpus-based Learning of Analogies and Semantic Relations. CoRR abs/cs/0508103 (2005)
- 2004
- [c54]Michael L. Littman, Nishkam Ravi, Eitan Fenson, Richard Howard: An Instance-Based State Representation for Network Repair. AAAI 2004: 287-292
- [c53]Michael L. Littman, Nishkam Ravi, Eitan Fenson, Richard Howard: Reinforcement Learning for Autonomic Network Repair. ICAC 2004: 284-285
- [c52]Michael R. James, Satinder P. Singh, Michael L. Littman: Planning with predictive state representations. ICMLA 2004: 304-311
- [c51]Alexander L. Strehl, Michael L. Littman: An Empirical Evaluation of Interval Estimation for Markov Decision Processes. ICTAI 2004: 128-135
- 2003
- [j17]Stephen M. Majercik, Michael L. Littman: Contingent planning under uncertainty via stochastic satisfiability. Artif. Intell. 147(1-2): 119-162 (2003)
- [j16]Yukio Ohsawa, Peter McBurney, Simon Parsons, Christopher A. Miller, Alan C. Schultz, Jean Scholtz, Michael A. Goodrich, Eugene Santos Jr., Benjamin Bell, Charles Lee Isbell Jr., Michael L. Littman: AAAI-2002 Fall Symposium Series. AI Magazine 24(1): 95-98 (2003)
- [j15]Peter Stone, Robert E. Schapire, Michael L. Littman, János A. Csirik, David A. McAllester: Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. J. Artif. Intell. Res. (JAIR) 19: 209-242 (2003)
- [j14]Peter D. Turney, Michael L. Littman: Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans. Inf. Syst. 21(4): 315-346 (2003)
- [c50]
- [c49]Satinder P. Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, Peter Stone: Learning Predictive State Representations. ICML 2003: 712-719
- [c48]Peter D. Turney, Michael L. Littman, Jeffrey Bigham, Victor Shnayder: Combining independent modules in lexical multiple-choice problems. RANLP 2003: 101-110
- [c47]Michael L. Littman, Peter Stone: A polynomial-time nash equilibrium algorithm for repeated games. ACM Conference on Electronic Commerce 2003: 48-54
- [i6]Peter D. Turney, Michael L. Littman: Measuring Praise and Criticism: Inference of Semantic Orientation from Association. CoRR cs.CL/0309034 (2003)
- [i5]Peter D. Turney, Michael L. Littman, Jeffrey Bigham, Victor Shnayder: Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems. CoRR cs.CL/0309035 (2003)
- [i4]Peter D. Turney, Michael L. Littman: Learning Analogies and Semantic Relations. CoRR cs.LG/0307055 (2003)
- 2002
- [j13]Michael L. Littman, Greg A. Keim, Noam M. Shazeer: A probabilistic approach to solving crossword puzzles. Artif. Intell. 134(1-2): 23-55 (2002)
- [c46]Peter Stone, Robert E. Schapire, János A. Csirik, Michael L. Littman, David A. McAllester: ATTac-2001: A Learning, Autonomous Bidding Agent. AMEC 2002: 143-160
- [c45]Paul S. A. Reitsma, Peter Stone, János A. Csirik, Michael L. Littman: Self-Enforcing Strategic Demand Reduction. AMEC 2002: 289-306
- [c44]Paul S. A. Reitsma, Peter Stone, János A. Csirik, Michael L. Littman: Randomized strategic demand reduction: getting more by asking for less. AAMAS 2002: 162-163
- [c43]Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. ICML 2002: 546-553
- [c42]Michail G. Lagoudakis, Ronald Parr, Michael L. Littman: Least-Squares Methods in Reinforcement Learning for Control. SETN 2002: 249-260
- [i3]Peter D. Turney, Michael L. Littman: Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus. CoRR cs.LG/0212012 (2002)
- 2001
- [j12]Michael L. Littman: Value-function reinforcement learning in Markov games. Cognitive Systems Research 2(1): 55-66 (2001)
- [j11]Michail G. Lagoudakis, Michael L. Littman: Learning to Select Branching Rules in the DPLL Procedure for Satisfiability. Electronic Notes in Discrete Mathematics 9: 344-359 (2001)
- [j10]Peter Stone, Michael L. Littman, Satinder P. Singh, Michael J. Kearns: ATTac-2000: An Adaptive Autonomous Bidding Agent. J. Artif. Intell. Res. (JAIR) 15: 189-206 (2001)
- [j9]Michael L. Littman, Stephen M. Majercik, Toniann Pitassi: Stochastic Boolean Satisfiability. J. Autom. Reasoning 27(3): 251-296 (2001)
- [c41]Peter Stone, Michael L. Littman, Satinder P. Singh, Michael J. Kearns: ATTac-2000: an adaptive autonomous bidding agent. Agents 2001: 238-245
- [c40]
- [c39]
- [c38]Sanjoy Dasgupta, Michael L. Littman, David A. McAllester: PAC Generalization Bounds for Co-training. NIPS 2001: 375-382
- [c37]Michael L. Littman, Michael J. Kearns, Satinder P. Singh: An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. NIPS 2001: 817-823
- [c36]Michael L. Littman, Richard S. Sutton, Satinder P. Singh: Predictive Representations of State. NIPS 2001: 1555-1561
- [c35]Michael J. Kearns, Michael L. Littman, Satinder P. Singh: Graphical Models for Game Theory. UAI 2001: 253-260
- [c34]János A. Csirik, Michael L. Littman, Satinder P. Singh, Peter Stone: FAucS : An FCC Spectrum Auction Simulator for Autonomous Bidding Agents. WELCOM 2001: 139-151
- 2000
- [j8]Sebastian Thrun, Michael L. Littman: A Review of Reinforcement Learning. AI Magazine 21(1): 103-105 (2000)
- [j7]Satinder P. Singh, Tommi Jaakkola, Michael L. Littman, Csaba Szepesvári: Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms. Machine Learning 38(3): 287-308 (2000)
- [c33]Michail G. Lagoudakis, Michael L. Littman: Reinforcement Learning for Algorithm Selection. AAAI/IAAI 2000: 1081
- [c32]
- [c31]Jiefu Shi, Michael L. Littman: Abstraction Methods for Game Theoretic Poker. Computers and Games 2000: 333-345
- [c30]
- [c29]
- [c28]Michail G. Lagoudakis, Michael L. Littman: Algorithm Selection using Reinforcement Learning. ICML 2000: 511-518
- [c27]
1990 – 1999
- 1999
- [j6]Giuseppe De Giacomo, Marie desJardins, Dolores Cañamero, Glenn S. Wasson, Michael L. Littman, Gerard Allwein, Kim Marriott, Bernd Meyer, Barbara Webb, Tom Con: The AAAI Fall Symposia. AI Magazine 20(3): 87-89 (1999)
- [j5]Csaba Szepesvári, Michael L. Littman: A Unified Analysis of Value-Function-Based Reinforcement Learning Algorithms. Neural Computation 11(8): 2017-2060 (1999)
- [c26]Noam M. Shazeer, Michael L. Littman, Greg A. Keim: Solving Crossword Puzzles as Probabilistic Constraint Satisfaction. AAAI/IAAI 1999: 156-162
- [c25]Stephen M. Majercik, Michael L. Littman: Contingent Planning Under Uncertainty via Stochastic Satisfiability. AAAI/IAAI 1999: 549-556
- [c24]
- [c23]Greg A. Keim, Noam M. Shazeer, Michael L. Littman, Sushant Agarwal, Catherine M. Cheves, Joseph Fitzgerald, Jason Grosland, Fan Jiang, Shannon Pollard, Karl Weinmeister: PROVERB: The Probabilistic Cruciverbalist. AAAI/IAAI 1999: 710-717
- [c22]Michael L. Littman, Greg A. Keim, Noam M. Shazeer: Solving Crosswords with PROVERB. AAAI/IAAI 1999: 914-915
- 1998
- [j4]Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra: Planning and Acting in Partially Observable Stochastic Domains. Artif. Intell. 101(1-2): 99-134 (1998)
- [j3]Michael L. Littman, Judy Goldsmith, Martin Mundhenk: The Computational Complexity of Probabilistic Planning. J. Artif. Intell. Res. (JAIR) 9: 1-36 (1998)
- [c21]Stephen M. Majercik, Michael L. Littman: Using Caching to Solve Larger Probabilistic Planning Problems. AAAI/IAAI 1998: 954-959
- [c20]Stephen M. Majercik, Michael L. Littman: MAXPLAN: A New Approach to Probabilistic Planning. AIPS 1998: 86-93
- [c19]Michael L. Littman, Fan Jiang, Greg A. Keim: Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus. ICML 1998: 314-322
- [i2]Michael L. Littman, Judy Goldsmith, Martin Mundhenk: The Computational Complexity of Probabilistic Planning. CoRR cs.AI/9808101 (1998)
- 1997
- [c18]Michael L. Littman: Probabilistic Propositional Planning: Representations and Complexity. AAAI/IAAI 1997: 748-754
- [c17]Michael S. Fulkerson, Michael L. Littman, Greg A. Keim: Speeding Safely: Multi-Criteria Optimization in Probabilistic Planning. AAAI/IAAI 1997: 831
- [c16]Bob Rehder, Michael L. Littman, Susan T. Dumais, Thomas K. Landauer: Automatic 3-Language Cross-Language Information Retrieval with Latent Semantic Indexing. TREC 1997: 233-239
- [c15]Anthony R. Cassandra, Michael L. Littman, Nevin Lianwen Zhang: Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes. UAI 1997: 54-61
- [c14]Judy Goldsmith, Michael L. Littman, Martin Mundhenk: The Complexity of Plan Existence and Evaluation in Probabilistic Domains. UAI 1997: 182-189
- 1996
- [j2]Eugene Charniak, Glenn Carroll, John Adcock, Anthony R. Cassandra, Yoshihiko Gotoh, Jeremy Katz, Michael L. Littman, John McCann: Taggers for Parsers. Artif. Intell. 85(1-2): 45-57 (1996)
- [j1]Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore: Reinforcement Learning: A Survey. J. Artif. Intell. Res. (JAIR) 4: 237-285 (1996)
- [c13]Michael L. Littman, Csaba Szepesvári: A Generalized Reinforcement-Learning Model: Convergence and Applications. ICML 1996: 310-318
- [i1]Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore: Reinforcement Learning: A Survey. CoRR cs.AI/9605103 (1996)
- 1995
- [c12]Michael L. Littman, Anthony R. Cassandra, Leslie Pack Kaelbling: Learning Policies for Partially Observable Environments: Scaling Up. ICML 1995: 362-370
- [c11]Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra: Partially Observable Markov Decision Processes for Artificial Intelligence. KI 1995: 1-17
- [c10]Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra: Partially Observable Markov Decision Processes for Artificial Intelligence. Reasoning with Uncertainty in Robotics 1995: 146-163
- [c9]Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling: On the Complexity of Solving Markov Decision Problems. UAI 1995: 394-402
- 1994
- [c8]Anthony R. Cassandra, Leslie Pack Kaelbling, Michael L. Littman: Acting Optimally in Partially Observable Stochastic Domains. AAAI 1994: 1023-1028
- [c7]Michael L. Littman: Markov Games as a Framework for Multi-Agent Reinforcement Learning. ICML 1994: 157-163
- 1993
- [c6]Robert B. Allen, Pascal Obry, Michael L. Littman: An interface for navigating clustered document sets returned by queries. COOCS 1993: 166-171
- [c5]Justin A. Boyan, Michael L. Littman: Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach. NIPS 1993: 671-678
- 1992
- [c4]Laurence Brothers, James D. Hollan, Jakob Neilsen, Scott Stornetta, Steven P. Abney, George W. Furnas, Michael L. Littman: Supporting Informal Communication via Ephemeral Interest Groups. CSCW 1992: 84-90
- 1991
- [c3]Dennis E. Egan, Michael Lesk, R. Daniel Ketchum, Carol C. Lochbaum, Joel R. Remde, Michael L. Littman, Thomas K. Landauer: Hypertext for the Electronic Library? CORE Sample Results. Hypertext 1991: 299-312
- [c2]Michael L. Littman, David H. Ackley: Adaptation in Constant Utility Non-Stationary Environments. ICGA 1991: 136-142
1980 – 1989
- 1989
- [c1]David H. Ackley, Michael L. Littman: Generalization and Scaling in Reinforcement Learning. NIPS 1989: 550-557
Coauthor Index
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last updated on 2013-10-02 11:10 CEST by the dblp team