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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