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Michael L. Littman - Publications

Affiliations: 
Computer Science Brown University, Providence, RI 
 Rutgers University, New Brunswick, New Brunswick, NJ, United States 
Area:
Artificial Intelligence, Machine Learning
Website:
www.cs.brown.edu/~mlittman
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67 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2023 Baker MM, New A, Aguilar-Simon M, Al-Halah Z, Arnold SMR, Ben-Iwhiwhu E, Brna AP, Brooks E, Brown RC, Daniels Z, Daram A, Delattre F, Dellana R, Eaton E, Fu H, ... ... Littman ML, et al. A domain-agnostic approach for characterization of lifelong learning systems. Neural Networks : the Official Journal of the International Neural Network Society. 160: 274-296. PMID 36709531 DOI: 10.1016/j.neunet.2023.01.007  0.323
2019 Abel D, Arumugam D, Asadi K, Jinnai Y, Littman ML, Wong LL. State Abstraction as Compression in Apprenticeship Learning Proceedings of the Aaai Conference On Artificial Intelligence. 33: 3134-3142. DOI: 10.1609/AAAI.V33I01.33013134  0.315
2017 Morris A, MacGlashan J, Littman ML, Cushman F. Evolution of flexibility and rigidity in retaliatory punishment. Proceedings of the National Academy of Sciences of the United States of America. PMID 28893996 DOI: 10.1073/Pnas.1704032114  0.364
2017 Ho MK, MacGlashan J, Littman ML, Cushman F. Social is special: A normative framework for teaching with and learning from evaluative feedback. Cognition. PMID 28341268 DOI: 10.1016/J.Cognition.2017.03.006  0.401
2016 Loftin R, Peng B, MacGlashan J, Littman ML, Taylor ME, Huang J, Roberts DL. Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning Autonomous Agents and Multi-Agent Systems. 30: 30-59. DOI: 10.1007/S10458-015-9283-7  0.427
2015 Littman ML. Reinforcement learning improves behaviour from evaluative feedback. Nature. 521: 445-51. PMID 26017443 DOI: 10.1038/Nature14540  0.469
2015 MacGlashan J, Littman ML. Between imitation and intention learning Ijcai International Joint Conference On Artificial Intelligence. 2015: 3692-3698.  0.347
2014 Loftin R, MacGlashan J, Peng B, Taylor ME, Littman ML, Huang J, Roberts DL. A strategy-aware technique for learning behaviors from discrete human feedback Proceedings of the National Conference On Artificial Intelligence. 2: 937-943.  0.334
2012 Walsh TJ, Littman ML, Borgida A. Learning web-service task descriptions from traces Web Intelligence and Agent Systems. 10: 397-421. DOI: 10.3233/Wia-2012-0254  0.355
2012 Vlassis N, Littman ML, Barber D. On the computational complexity of stochastic controller optimization in POMDPs Acm Transactions On Computation Theory. 4. DOI: 10.1145/2382559.2382563  0.322
2012 Weinstein A, Littman ML. Bandit-based planning and learning in continuous-action markov decision processes Icaps 2012 - Proceedings of the 22nd International Conference On Automated Planning and Scheduling. 306-314.  0.379
2011 Clyde MA, Ghosh J, Littman ML. Bayesian adaptive sampling for variable selection and model averaging Journal of Computational and Graphical Statistics. 20: 80-101. DOI: 10.1198/Jcgs.2010.09049  0.308
2011 Yaman F, Walsh TJ, Littman ML, Desjardins M. Democratic approximation of lexicographic preference models Artificial Intelligence. 175: 1290-1307. DOI: 10.1016/J.Artint.2010.11.012  0.398
2011 Whiteson S, Littman ML. Introduction to the special issue on empirical evaluations in reinforcement learning Machine Learning. 84: 1-6. DOI: 10.1007/S10994-011-5255-6  0.422
2011 Li L, Littman ML, Walsh TJ, Strehl AL. Knows what it knows: A framework for self-aware learning Machine Learning. 82: 399-443. DOI: 10.1007/S10994-010-5225-4  0.447
2011 Yuan C, Lim H, Littman ML. Most Relevant Explanation: Computational complexity and approximation methods Annals of Mathematics and Artificial Intelligence. 61: 159-183. DOI: 10.1007/S10472-011-9260-Z  0.339
2010 Li L, Littman ML. Reducing reinforcement learning to KWIK online regression Annals of Mathematics and Artificial Intelligence. 58: 217-237. DOI: 10.1007/S10472-010-9201-2  0.487
2010 Subramanian K, Littman ML. Efficient apprenticeship learning with smart humans Aaai Workshop - Technical Report. 29-30.  0.33
2010 Walsh TJ, Goschin S, Littman ML. Integrating sample-based planning and model-based reinforcement learning Proceedings of the National Conference On Artificial Intelligence. 1: 612-617.  0.311
2010 Walsh TJ, Subramanian K, Littman ML, Diuk C. Generalizing apprenticeship learning across hypothesis classes Icml 2010 - Proceedings, 27th International Conference On Machine Learning. 1119-1126.  0.337
2009 Littman ML. A tutorial on partially observable Markov decision processes Journal of Mathematical Psychology. 53: 119-125. DOI: 10.1016/J.Jmp.2009.01.005  0.421
2009 Walsh TJ, Nouri A, Li L, Littman ML. Learning and planning in environments with delayed feedback Autonomous Agents and Multi-Agent Systems. 18: 83-105. DOI: 10.1007/S10458-008-9056-7  0.481
2009 Brunskill E, Leffler BR, Li H, Littman ML, Roy N. Provably efficient learning with typed parametric models Journal of Machine Learning Research. 10: 1955-1988.  0.327
2009 Walsh TJ, Szita I, Diuk C, Littman ML. Exploring compact reinforcement-learning representations with linear regression Proceedings of the 25th Conference On Uncertainty in Artificial Intelligence, Uai 2009. 591-598.  0.348
2009 Asmuth J, Li L, Littman ML, Nouri A, Wingate D. A Bayesian sampling approach to exploration in reinforcement learning Proceedings of the 25th Conference On Uncertainty in Artificial Intelligence, Uai 2009. 19-26.  0.321
2009 Strehl AL, Littman ML. Online linear regression and its application to model-based reinforcement learning Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference.  0.344
2009 Li L, Littman ML, Mansley CR. Online exploration in least-squares policy iteration Proceedings of the International Joint Conference On Autonomous Agents and Multiagent Systems, Aamas. 1: 539-545.  0.382
2008 Strehl AL, Littman ML. An analysis of model-based Interval Estimation for Markov Decision Processes Journal of Computer and System Sciences. 74: 1309-1331. DOI: 10.1016/J.Jcss.2007.08.009  0.426
2008 Roberts DL, Isbell CL, Littman ML. Optimization problems involving collections of dependent objects Annals of Operations Research. 163: 255-270. DOI: 10.1007/S10479-008-0350-1  0.337
2008 Li L, Littman ML. Efficient value-function approximation via online linear regression 10th International Symposium On Artificial Intelligence and Mathematics, Isaim 2008. 8P.  0.312
2008 Asmuth J, Littman ML, Zinkov R. Potential-based shaping in model-based reinforcement learning Proceedings of the National Conference On Artificial Intelligence. 2: 604-609.  0.348
2008 Brunskill E, Leffler BR, Li L, Littman ML, Roy N. CORL: A continuous-state offset-dynamics reinforcement learner Proceedings of the 24th Conference On Uncertainty in Artificial Intelligence, Uai 2008. 53-61.  0.311
2008 Diuk C, Cohen A, Littman ML. An object-oriented representation for efficient reinforcement learning Proceedings of the 25th International Conference On Machine Learning. 240-247.  0.344
2008 Babes M, De Cote EM, Littman ML. Social reward shaping in the Prisoner's dilemma Proceedings of the International Joint Conference On Autonomous Agents and Multiagent Systems, Aamas. 3: 1357-1360.  0.314
2007 Zinkevich M, Greenwald A, Littman ML. A hierarchy of prescriptive goals for multiagent learning Artificial Intelligence. 171: 440-447. DOI: 10.1016/J.Artint.2007.02.005  0.471
2007 Greenwald A, Littman ML. Introduction to the special issue on learning and computational game theory Machine Learning. 67: 3-6. DOI: 10.1007/S10994-007-0770-1  0.458
2007 Strehl AL, Diuk C, Littman ML. Efficient structure learning in factored-state MDPs Proceedings of the National Conference On Artificial Intelligence. 1: 645-650.  0.339
2007 Walsh TJ, Nouri A, Li H, Littman ML. Planning and learning in environments with delayed feedback Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4701: 442-453.  0.398
2007 Leffler BR, Littman ML, Edmunds T. Efficient reinforcement learning with relocatable action models Proceedings of the National Conference On Artificial Intelligence. 1: 572-577.  0.353
2006 Diuk C, Strehl AL, Littman ML. A hierarchical approach to efficient reinforcement learning in deterministic domains Proceedings of the International Conference On Autonomous Agents. 2006: 313-319. DOI: 10.1145/1160633.1160686  0.389
2006 Strehl AL, Mesterharm C, Littman ML, Hirsh H. Experience-efficient learning in associative bandit problems Acm International Conference Proceeding Series. 148: 889-896. DOI: 10.1145/1143844.1143956  0.342
2006 Strehl AL, Lihong L, Wiewiora E, Langford J, Littman ML. PAC model-free reinforcement learning Acm International Conference Proceeding Series. 148: 881-888. DOI: 10.1145/1143844.1143955  0.343
2006 Strehl AL, Li L, Littman ML. Incremental model-based learners with formal learning-time guarantees Proceedings of the 22nd Conference On Uncertainty in Artificial Intelligence, Uai 2006. 485-493.  0.352
2006 Strehl AL, Li H, Littman ML. PAC reinforcement learning bounds for RTDP and Rand-RTDP Aaai Workshop - Technical Report. 50-56.  0.332
2005 Cassimatis N, Luke S, Levy SD, Gayler R, Kanerva P, Eliasmith C, Bickmore T, Schultz AC, Davis R, Landay J, Miller R, Saund E, Stahovich T, Littman M, Singh S, et al. Reports on the 2004 AAAI Fall Symposia Ai Magazine. 26: 98-102. DOI: 10.1609/Aimag.V26I1.1805  0.362
2005 Turney PD, Littman ML. Corpus-based learning of analogies and semantic relations Machine Learning. 60: 251-278. DOI: 10.1007/S10994-005-0913-1  0.378
2004 Strehl AL, Littman ML. An empirical evaluation of interval estimation for Markov decision processes Proceedings - International Conference On Tools With Artificial Intelligence, Ictai. 128-135. DOI: 10.1109/ICTAI.2004.28  0.332
2004 James MR, Singh S, Littman ML. Planning with predictive state representations Proceedings of the 2004 International Conference On Machine Learning and Applications, Icmla '04. 304-311.  0.326
2003 Stone P, Schapire RE, Littman ML, Csirik JA, McAllester D. Decision-theoretic bidding based on learned density Models in simultaneous, interacting auctions Journal of Artificial Intelligence Research. 19: 209-242. DOI: 10.1613/Jair.1200  0.457
2003 Majercik SM, Littman ML. Contingent planning under uncertainty via stochastic satisfiability Artificial Intelligence. 147: 119-162. DOI: 10.1016/S0004-3702(02)00379-X  0.372
2003 Littman ML. Tutorial: Learning topics in game-theoretic decision making Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2777: 1.  0.322
2003 Singh S, Littman ML, Jong NK, Pardoe D, Stone P. Learning Predictive State Representations Proceedings, Twentieth International Conference On Machine Learning. 2: 712-719.  0.319
2002 Littman ML, Keim GA, Shazeer N. A probabilistic approach to solving crossword puzzles Artificial Intelligence. 134: 23-55. DOI: 10.1016/S0004-3702(01)00114-X  0.392
2002 Stone P, Schapire RE, Csirik JA, Littman ML, McAllester D. ATTac-2001: A learning, autonomous bidding agent Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2531: 143-160. DOI: 10.1007/3-540-36378-5_9  0.422
2002 Reitsma PSA, Stone P, Csirik JA, Littman ML. Self-enforcing strategic demand reduction Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2531: 289-306. DOI: 10.1007/3-540-36378-5_18  0.327
2002 Lagoudakis MG, Parr R, Littman ML. Least-squares methods in reinforcement learning for control Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2308: 249-260.  0.37
2001 Stone P, Littman ML, Singh S, Kearns M. ATTac-2000: An adaptive autonomous bidding agent Journal of Artificial Intelligence Research. 15: 189-206. DOI: 10.1613/Jair.865  0.361
2001 Littman ML, Majercik SM, Pitassi T. Stochastic boolean satisfiability Journal of Automated Reasoning. 27: 251-296. DOI: 10.1023/A:1017584715408  0.408
2001 Lagoudakis MG, Littman ML. Learning to select branching rules in the DPLL procedure for satisfiability Electronic Notes in Discrete Mathematics. 9: 344-359. DOI: 10.1016/S1571-0653(04)00332-4  0.424
2001 Littman ML. Value-function reinforcement learning in Markov games Cognitive Systems Research. 2: 55-66. DOI: 10.1016/S1389-0417(01)00015-8  0.462
2000 Thrun S, Littman ML. A Review of Reinforcement Learning Ai Magazine. 21: 103-105. DOI: 10.1609/Aimag.V21I1.1501  0.37
2000 Singh S, Jaakkola T, Littman ML, Szepesvári C. Convergence results for single-step on-policy reinforcement-learning algorithms Machine Learning. 38: 287-308. DOI: 10.1023/A:1007678930559  0.436
1999 Szepesvári C, Littman ML. A unified analysis of value-function-based reinforcement-learning algorithms Neural Computation. 11: 2017-2060. PMID 10578043 DOI: 10.1162/089976699300016070  0.466
1998 Littman ML, Goldsmith J, Mundhenk M. The computational complexity of probabilistic planning Journal of Artificial Intelligence Research. 9: 1-36. DOI: 10.1613/Jair.505  0.32
1998 Kaelbling LP, Littman ML, Cassandra AR. Planning and acting in partially observable stochastic domains Artificial Intelligence. 101: 99-134. DOI: 10.1016/S0004-3702(98)00023-X  0.598
1996 Kaelbling LP, Littman ML, Moore AW. Reinforcement learning: A survey Journal of Artificial Intelligence Research. 4: 237-285. DOI: 10.1613/Jair.301  0.628
1996 Kaelbling LP, Littman ML, Cassandra AR. Partially observable Markov decision processes for artificial intelligencea Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1093: 146-163. DOI: 10.1007/BFb0013957  0.583
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