Michael L. Littman

294 papers A* 71A 23B 10C 2Misc 3Journal 122Unranked 61

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	AI+HW 2035: Shaping the Next Decade. CoRR Deming Chen, Jason Cong, Azalia Mirhoseini, Christos Kozyrakis, Subhasish Mitra, Jinjun Xiong, Cliff Young, Anima Anandkumar, Michael L. Littman, Aron Kirschen, Sophia Shao, Serge Leef, Naresh R. Shanbhag, Dejan S. Milojicic, Michael Schulte, Gert Cauwenberghs, Jerry M. Chow, Tri Dao, Kailash Gopalakrishnan, Richard Ho, Hoshik Kim, Kunle Olukotun, David Z. Pan, Mark Ren, Dan Roth, Aarti Singh, Yizhou Sun, Yusu Wang, Yann LeCun, Ruchir Puri
2026	—	`conf`	Learning Persistence & Resistance from History & SIGCSE Reads. SIGCSE (2) Rebecca Bates, Judy Goldsmith, Valerie Summet, Nanette Veilleux, Katie Johnson, Michael L. Littman, Kyla A. McMullen, Jeremy A. Magruder Waisome
2025	A*	`conf`	Enabling End Users to Program Robots Using Reinforcement Learning. HRI Tewodros W. Ayalew, Jennifer Wang, Michael L. Littman, Blase Ur, Sarah Sebo
2025	A*	`conf`	How Humans Communicate Programming Tasks in Natural Language and Implications For End-User Programming with LLMs. CHI Madison Pickering, Helena Williams, Alison Gan, Weijia He, Hyojae Park, Francisco Piedrahita Velez, Michael L. Littman, Blase Ur
2025	J	`jnl`	Knowledge Retention for Continual Model-Based Reinforcement Learning. CoRR Yixiang Sun, Haotian Fu, Michael L. Littman, George Dimitri Konidaris
2024	A*	`conf`	Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. NeurIPS Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris
2024	J	`jnl`	Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. CoRR Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris
2024	J	`jnl`	Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages. CoRR Max Zuo, Francisco Piedrahita Velez, Xiaochen Li, Michael L. Littman, Stephen H. Bach
2023	J	`jnl`	Software Engineering of Machine Learning Systems. Commun. ACM Charles Isbell, Michael L. Littman, Peter Norvig
2022	J	`jnl`	Designing Rewards for Fast Learning. CoRR Henry Sowerby, Zhiyuan Zhou, Michael L. Littman
2022	J	`jnl`	Does DQN really learn? Exploring adversarial training schemes in Pong. CoRR Bowen He, Sreehari Rammohan, Jessica Zosa Forde, Michael L. Littman
2022	J	`jnl`	Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex. CoRR Charles Lovering, Jessica Zosa Forde, George Konidaris, Ellie Pavlick, Michael L. Littman
2022	A*	`conf`	Evaluation beyond Task Performance: Analyzing Concepts in AlphaZero in Hex. NeurIPS Charles Lovering, Jessica Forde, George Konidaris, Ellie Pavlick, Michael L. Littman
2022	—	`conf`	Explaining Why: How Instructions and User Interfaces Impact Annotator Rationales When Labeling Text Data. NAACL-HLT Jamar L. Sullivan Jr., William Brackenbury, Andrew McNut, Kevin Bryson, Kwam Byll, Yuxin Chen, Michael L. Littman, Chenhao Tan, Blase Ur
2022	A*	`conf`	Faster Deep Reinforcement Learning with Slower Online Network. NeurIPS Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Taesup Kim, Michael L. Littman, Alexander J. Smola
2022	J	`jnl`	Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report. CoRR Michael L. Littman, Ifeoma Ajunwa, Guy Berger, Craig Boutilier, Morgan Currie, Finale Doshi-Velez, Gillian K. Hadfield, Michael C. Horowitz, Charles Isbell, Hiroaki Kitano, Karen Levy, Terah Lyons, Melanie Mitchell, Julie Shah, Steven A. Sloman, Shannon Vallor, Toby Walsh
2022	J	`jnl`	Helping Users Debug Trigger-Action Programs. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Lefan Zhang, Cyrus Zhou, Michael L. Littman, Blase Ur, Shan Lu
2022	A*	`conf`	On the (In)Tractability of Reinforcement Learning for LTL Objectives. IJCAI Cambridge Yang, Michael L. Littman, Michael Carbin
2022	A*	`conf`	On the Expressivity of Markov Reward (Extended Abstract). IJCAI David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
2022	J	`jnl`	Reward-Predictive Clustering. CoRR Lucas Lehnert, Michael J. Frank, Michael L. Littman
2022	J	`jnl`	Specifying Behavior Preference with Tiered Reward Functions. CoRR Zhiyuan Zhou, Henry Sowerby, Michael L. Littman
2021	J	`jnl`	Bad-Policy Density: A Measure of Reinforcement Learning Hardness. CoRR David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong
2021	J	`jnl`	Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program. CoRR Jeff Druce, James Niehaus, Vanessa Moody, David D. Jensen, Michael L. Littman
2021	J	`jnl`	Collusion rings threaten the integrity of computer science research. Commun. ACM Michael L. Littman
2021	J	`jnl`	Control of mental representations in human planning. CoRR Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, Jonathan D. Cohen, Thomas L. Griffiths
2021	J	`jnl`	Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback. CoRR Ishaan Shah, David Halpern, Kavosh Asadi, Michael L. Littman
2021	J	`jnl`	Deep Q-Network with Proximal Iteration. CoRR Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Michael L. Littman, Alexander J. Smola
2021	A*	`conf`	Deep Radial-Basis Value Functions for Continuous Control. AAAI Kavosh Asadi, Neev Parikh, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman
2021	J	`jnl`	Learning Generalizable Behavior via Visual Rewrite Rules. CoRR Yiheng Xie, Mingxuan Li, Shangqun Yu, Michael L. Littman
2021	A*	`conf`	Lipschitz Lifelong Reinforcement Learning. AAAI Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman
2021	A*	`conf`	On the Expressivity of Markov Reward. NeurIPS David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
2021	J	`jnl`	On the Expressivity of Markov Reward. CoRR David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
2021	J	`jnl`	Reinforcement Learning for General LTL Objectives Is Intractable. CoRR Cambridge Yang, Michael L. Littman, Michael Carbin
2021	A*	`conf`	Towards Sample Efficient Agents through Algorithmic Alignment (Student Abstract). AAAI Mingxuan Li, Michael L. Littman
2021	A*	`conf`	Understanding Trigger-Action Programs Through Novel Visualizations of Program Differences. CHI Valerie Zhao, Lefan Zhang, Bo Wang, Michael L. Littman, Shan Lu, Blase Ur
2020	A	`conf`	Applying prerequisite structure inference to adaptive testing. LAK Sam Saarinen, Evan Cater, Michael L. Littman
2020	—	`conf`	Context-Driven Satirical News Generation. Fig-Lang@ACL Zachary Horvitz, Nam Do, Michael L. Littman
2020	J	`jnl`	Deep RBF Value Functions for Continuous Control. CoRR Kavosh Asadi, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman
2020	J	`jnl`	Lipschitz Lifelong Reinforcement Learning. CoRR Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman
2020	A*	`conf`	People Do Not Just Plan, They Plan to Plan. AAAI Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths
2020	J	`jnl`	Reward-predictive representations generalize across tasks in reinforcement learning. PLoS Comput. Biol. Lucas Lehnert, Michael L. Littman, Michael J. Frank
2020	J	`jnl`	Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning. J. Mach. Learn. Res. Lucas Lehnert, Michael L. Littman
2020	A*	`conf`	Teaching a Robot Tasks of Arbitrary Complexity via Human Feedback. HRI Guan Wang, Carl Trimbach, Jun Ki Lee, Mark K. Ho, Michael L. Littman
2020	J	`jnl`	The Efficiency of Human Cognition Reflects Planned Information Processing. CoRR Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths
2020	J	`jnl`	Towards Sample Efficient Agents through Algorithmic Alignment. CoRR Mingxuan Li, Michael L. Littman
2020	J	`jnl`	Trace2TAP: Synthesizing Trigger-Action Programs from Traces of Behavior. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Lefan Zhang, Weijia He, Olivia Morkved, Valerie Zhao, Michael L. Littman, Shan Lu, Blase Ur
2020	A	`conf`	Value Preserving State-Action Abstractions. AISTATS David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman
2019	J	`jnl`	Combating the Compounding-Error Problem with a Multi-step Model. CoRR Kavosh Asadi, Dipendra Misra, Seungchan Kim, Michael L. Littman
2019	J	`jnl`	Decision trees. Inroads John Barr, Michael L. Littman, Marie desJardins
2019	J	`jnl`	Deep Reinforcement Learning from Policy-Dependent Human Feedback. CoRR Dilip Arumugam, Jun Ki Lee, Sophie Saskin, Michael L. Littman
2019	A*	`conf`	DeepMellow: Removing the Need for a Target Network in Deep Q-Learning. IJCAI Seungchan Kim, Kavosh Asadi, Michael L. Littman, George Dimitri Konidaris
2019	—	`conf`	Evidence Humans Provide When Explaining Data-Labeling Decisions. INTERACT (3) Judah Newman, Bowen Wang, Valerie Zhao, Amy Zeng, Michael L. Littman, Blase Ur
2019	A*	`conf`	Finding Options that Minimize Planning Time. ICML Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris
2019	A*	`conf`	How Users Interpret Bugs in Trigger-Action Programming. CHI Will Brackenbury, Abhimanyu Deora, Jillian Ritchey, Jason Vallee, Weijia He, Guan Wang, Michael L. Littman, Blase Ur
2019	J	`jnl`	Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging. CoRR John R. Zech, Jessica Zosa Forde, Michael L. Littman
2019	J	`jnl`	Interactive Learning of Environment Dynamics for Sequential Tasks. CoRR Robert T. Loftin, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts
2019	J	`jnl`	ReNeg and Backseat Driver: Learning from Demonstration with Continuous Human Feedback. CoRR Jacob Beck, Zoe Papakipos, Michael L. Littman
2019	A	`conf`	Removing the Target Network from Deep Q-Networks with the Mellowmax Operator. AAMAS Seungchan Kim, Kavosh Asadi, Michael L. Littman, George Dimitri Konidaris
2019	B	`conf`	Stackelberg Punishment and Bully-Proofing Autonomous Vehicles. ICSR Matt Cooper, Jun Ki Lee, Jacob Beck, Joshua D. Fishman, Michael Gillett, Zoë Papakipos, Aaron Zhang, Jerome Ramos, Aansh Shah, Michael L. Littman
2019	J	`jnl`	Stackelberg Punishment and Bully-Proofing Autonomous Vehicles. CoRR Matt Cooper, Jun Ki Lee, Jacob Beck, Joshua D. Fishman, Michael Gillett, Zoë Papakipos, Aaron Zhang, Jerome Ramos, Aansh Shah, Michael L. Littman
2019	A*	`conf`	State Abstraction as Compression in Apprenticeship Learning. AAAI David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong
2019	J	`jnl`	Successor Features Support Model-based and Model-free Reinforcement Learning. CoRR Lucas Lehnert, Michael L. Littman
2019	J	`jnl`	Teaching with IMPACT. CoRR Carl Trimbach, Michael L. Littman
2019	A*	`conf`	The Expected-Length Model of Options. IJCAI David Abel, John Winder, Marie desJardins, Michael L. Littman
2019	J	`jnl`	Theory of Minds: Understanding Behavior in Groups Through Inverse Planning. CoRR Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum
2019	A*	`conf`	Theory of Minds: Understanding Behavior in Groups through Inverse Planning. AAAI Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum
2018	A*	`conf`	Bandit-Based Solar Panel Control. AAAI David Abel, Edward C. Williams, Stephen Brawner, Emily Reif, Michael L. Littman
2018	J	`jnl`	Curriculum Design for Machine Learners in Sequential Decision Tasks. IEEE Trans. Emerg. Top. Comput. Intell. Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor
2018	B	`conf`	Effectively Learning from Pedagogical Demonstrations. CogSci Mark K. Ho, Michael L. Littman, Fiery Cushman, Joseph L. Austerweil
2018	J	`jnl`	Equivalence Between Wasserstein and Value-Aware Model-based Reinforcement Learning. CoRR Kavosh Asadi, Evan Cater, Dipendra Misra, Michael L. Littman
2018	J	`jnl`	Evolutionary huffman encoding. Inroads Marie desJardins, Michael L. Littman
2018	J	`jnl`	Finding Options that Minimize Planning Time. CoRR Yuu Jinnai, David Abel, Michael L. Littman, George Dimitri Konidaris
2018	A*	`conf`	Lipschitz Continuity in Model-based Reinforcement Learning. ICML Kavosh Asadi, Dipendra Misra, Michael L. Littman
2018	J	`jnl`	Lipschitz Continuity in Model-based Reinforcement Learning. CoRR Kavosh Asadi, Dipendra Misra, Michael L. Littman
2018	J	`jnl`	Measuring and Characterizing Generalization in Deep Reinforcement Learning. CoRR Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael L. Littman, David D. Jensen
2018	J	`jnl`	Mitigating Planner Overfitting in Model-Based Reinforcement Learning. CoRR Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman
2018	J	`jnl`	Personalized Education at Scale. CoRR Sam Saarinen, Evan Cater, Michael L. Littman
2018	A*	`conf`	Policy and Value Transfer in Lifelong Reinforcement Learning. ICML David Abel, Yuu Jinnai, Yue (Sophie) Guo, George Dimitri Konidaris, Michael L. Littman
2018	A*	`conf`	State Abstractions for Lifelong Reinforcement Learning. ICML David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman
2018	J	`jnl`	Towards a Simple Approach to Multi-step Model-based Reinforcement Learning. CoRR Kavosh Asadi, Evan Cater, Dipendra Misra, Michael L. Littman
2018	J	`jnl`	Transfer with Model Features in Reinforcement Learning. CoRR Lucas Lehnert, Michael L. Littman
2017	J	`jnl`	Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning. CoRR Lucas Lehnert, Stefanie Tellex, Michael L. Littman
2017	A*	`conf`	An Alternative Softmax Operator for Reinforcement Learning. ICML Kavosh Asadi, Michael L. Littman
2017	J	`jnl`	Ask Me Anything about MOOCs. AI Mag. Doug Fisher, Charles Lee Isbell Jr., Michael L. Littman, Michael Wollowski, Todd W. Neller, Jim Boerkoel
2017	A	`conf`	Curriculum Design for Machine Learners in Sequential Decision Tasks. AAMAS Bei Peng, James MacGlashan, Robert T. Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor
2017	J	`jnl`	Environment-Independent Task Specifications via GLTL. CoRR Michael L. Littman, Ufuk Topcu, Jie Fu, Charles Lee Isbell Jr., Min Wen, James MacGlashan
2017	A*	`conf`	Interactive Learning from Policy-Dependent Human Feedback. ICML James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman
2017	J	`jnl`	Interactive Learning from Policy-Dependent Human Feedback. CoRR James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, David L. Roberts, Matthew E. Taylor, Michael L. Littman
2017	J	`jnl`	Latent Attention Networks. CoRR Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman
2017	J	`jnl`	Learning Approximate Stochastic Transition Models. CoRR Yuhang Song, Christopher Grimm, Xianming Wang, Michael L. Littman
2017	J	`jnl`	Mean Actor Critic. CoRR Kavosh Asadi, Cameron Allen, Melrose Roderick, Abdel-rahman Mohamed, George Dimitri Konidaris, Michael L. Littman
2017	J	`jnl`	Near Optimal Behavior via Approximate State Abstraction. CoRR David Abel, D. Ellis Hershkowitz, Michael L. Littman
2017	A*	`conf`	Planning with Abstract Markov Decision Processes. ICAPS Nakul Gopalan, Marie desJardins, Michael L. Littman, James MacGlashan, Shawn Squire, Stefanie Tellex, John Winder, Lawson L. S. Wong
2017	J	`jnl`	Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting. CoRR Christopher Grimm, Yuhang Song, Michael L. Littman
2017	B	`conf`	Teaching by Intervention: Working Backwards, Undoing Mistakes, or Correcting Mistakes? CogSci Mark K. Ho, Michael L. Littman, Joseph L. Austerweil
2016	A	`conf`	A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans. AAMAS Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor
2016	J	`jnl`	A New Softmax Operator for Reinforcement Learning. CoRR Kavosh Asadi, Michael L. Littman
2016	B	`conf`	Coordinate to cooperate or compete: Abstract goals and joint intentions in social interaction. CogSci Max Kleiman-Weiner, Mark K. Ho, Joseph L. Austerweil, Michael L. Littman, Josh Tenenbaum
2016	B	`conf`	Feature-based Joint Planning and Norm Learning in Collaborative Games. CogSci Mark K. Ho, James MacGlashan, Amy Greenwald, Michael L. Littman, Elizabeth Hilliard, Carl Trimbach, Stephen Brawner, Josh Tenenbaum, Max Kleiman-Weiner, Joseph L. Austerweil
2016	—	`conf`	Learning User's Preferred Household Organization via Collaborative Filtering Methods. IntRS@RecSys Stephen Brawner, Michael L. Littman
2016	J	`jnl`	Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning. Auton. Agents Multi Agent Syst. Robert T. Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts
2016	A*	`conf`	Near Optimal Behavior via Approximate State Abstraction. ICML David Abel, D. Ellis Hershkowitz, Michael L. Littman
2016	B	`conf`	Peer Reviewing Short Answers using Comparative Judgement. L@S Pushkar Kolhe, Michael L. Littman, Charles L. Isbell Jr.
2016	C	`conf`	Reinforcement Learning as a Framework for Ethical Decision Making. AAAI Workshop: AI, Ethics, and Society David Abel, James MacGlashan, Michael L. Littman
2016	—	`conf`	Showing versus doing: Teaching by demonstration. NIPS Mark K. Ho, Michael L. Littman, James MacGlashan, Fiery Cushman, Joseph L. Austerweil
2016	—	`conf`	Towards Behavior-Aware Model Learning from Human-Generated Trajectories. AAAI Fall Symposia Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts
2016	A*	`conf`	Trigger-Action Programming in the Wild: An Analysis of 200, 000 IFTTT Recipes. CHI Blase Ur, Melwyn Pak Yong Ho, Stephen Brawner, Jiyun Lee, Sarah Mennicken, Noah Picard, Diane Schulze, Michael L. Littman
2015	A*	`conf`	Between Imitation and Intention Learning. IJCAI James MacGlashan, Michael L. Littman
2015	—	`conf`	Grounding English Commands to Reward Functions. Robotics: Science and Systems James MacGlashan, Monica Babes-Vroman, Marie desJardins, Michael L. Littman, Smaranda Muresan, Shawn Squire, Stefanie Tellex, Dilip Arumugam, Lei Yang
2015	J	`jnl`	Reinforcement learning improves behaviour from evaluative feedback. Nat. Michael L. Littman
2015	B	`conf`	Teaching with Rewards and Punishments: Reinforcement or Communication? CogSci Mark K. Ho, Michael L. Littman, Fiery Cushman, Joseph L. Austerweil
2015	J	`jnl`	Who speaks for AI? AI Matters Eric Eaton, Tom Dietterich, Maria L. Gini, Barbara J. Grosz, Charles L. Isbell Jr., Subbarao Kambhampati, Michael L. Littman, Francesca Rossi, Stuart Russell, Peter Stone, Toby Walsh, Michael J. Wooldridge
2014	A*	`conf`	A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback. AAAI Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, Jeff Huang, David L. Roberts
2014	B	`conf`	Flexible theft and resolute punishment: Evolutionary dynamics of social behavior among reinforcement-learning agents. CogSci James MacGlashan, Michael L. Littman, Fiery Cushman
2014	B	`conf`	Learning something from nothing: Leveraging implicit human feedback strategies. RO-MAN Robert Tyler Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts
2014	A*	`conf`	Practical trigger-action programming in the smart home. CHI Blase Ur, Elyse McManus, Melwyn Pak Yong Ho, Michael L. Littman
2014	—	`conf`	Quantifying Uncertainty in Batch Personalized Sequential Decision Making. AAAI Workshop: Modern Artificial Intelligence for Health Analytics Vukosi Marivate, Jessica Chemali, Emma Brunskill, Michael L. Littman
2013	A*	`conf`	AAAI-13 Preface. AAAI Marie desJardins, Michael L. Littman
2013	—	`conf`	An Ensemble of Linearly Combined Reinforcement-Learning Agents. AAAI (Late-Breaking Developments) Vukosi Marivate, Michael L. Littman
2013	—	`conf`	Coco-Q: Learning in Stochastic Games with Side Payments. ICML (3) Eric Sodomka, Elizabeth Hilliard, Michael L. Littman, Amy Greenwald
2013	J	`jnl`	Graphical Models for Game Theory CoRR Michael J. Kearns, Michael L. Littman, Satinder Singh
2013	J	`jnl`	Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes CoRR Anthony R. Cassandra, Michael L. Littman, Nevin Lianwen Zhang
2013	J	`jnl`	On the Complexity of Solving Markov Decision Problems CoRR Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling
2013	A*	`conf`	Open-Loop Planning in Large-Scale Stochastic Domains. AAAI Ari Weinstein, Michael L. Littman
2013	A*	`ed.`	Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, July 14-18, 2013, Bellevue, Washington, USA. AAAI Marie desJardins, Michael L. Littman
2013	J	`jnl`	The Complexity of Plan Existence and Evaluation in Probabilistic Domains CoRR Judy Goldsmith, Michael L. Littman, Martin Mundhenk
2013	—	`conf`	The Cross-Entropy Method Optimizes for Quantiles. ICML (3) Sergiu Goschin, Ari Weinstein, Michael L. Littman
2012	J	`jnl`	A Bayesian Sampling Approach to Exploration in Reinforcement Learning CoRR John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate
2012	J	`jnl`	A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games CoRR Enrique Munoz de Cote, Michael L. Littman
2012	A	`conf`	A framework for modeling population strategies by depth of reasoning. AAMAS Michael Wunder, John Robert Yaros, Michael Kaisers, Michael L. Littman
2012	J	`jnl`	A new way to search game trees: technical perspective. Commun. ACM Michael L. Littman
2012	J	`jnl`	An Efficient Optimal-Equilibrium Algorithm for Two-player Game Trees. CoRR Michael L. Littman, Nishkam Ravi, Arjun Talwar, Martin Zinkevich
2012	A*	`conf`	Bandit-Based Planning and Learning in Continuous-Action Markov Decision Processes. ICAPS Ari Weinstein, Michael L. Littman
2012	J	`jnl`	CORL: A Continuous-state Offset-dynamics Reinforcement Learner CoRR Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy
2012	A*	`conf`	Covering Number as a Complexity Measure for POMDP Planning and Learning. AAAI Zongzhang Zhang, Michael L. Littman, Xiaoping Chen
2012	J	`jnl`	Exploring compact reinforcement-learning representations with linear regression CoRR Thomas J. Walsh, Istvan Szita, Carlos Diuk, Michael L. Littman
2012	J	`jnl`	Incremental Model-based Learners With Formal Learning-Time Guarantees. CoRR Alexander L. Strehl, Lihong Li, Michael L. Littman
2012	—	`conf`	Inducing Partially Observable Markov Decision Processes. ICGI Michael L. Littman
2012	J	`jnl`	Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search CoRR John Asmuth, Michael L. Littman
2012	J	`jnl`	Learning web-service task descriptions from traces. Web Intell. Agent Syst. Thomas J. Walsh, Michael L. Littman, Alexander Borgida
2012	J	`jnl`	On the Computational Complexity of Stochastic Controller Optimization in POMDPs. ACM Trans. Comput. Theory Nikos Vlassis, Michael L. Littman, David Barber
2012	—	`conf`	Planning in Reward-Rich Domains via PAC Bandits. EWRL Sergiu Goschin, Ari Weinstein, Michael L. Littman, Erick Chastain
2012	—	`conf`	Rollout-based Game-tree Search Outprunes Traditional Alpha-beta. EWRL Ari Weinstein, Michael L. Littman, Sergiu Goschin
2011	J	`jnl`	ATTac-2000: An Adaptive Autonomous Bidding Agent CoRR Michael J. Kearns, Michael L. Littman, Satinder Singh, Peter Stone
2011	A*	`conf`	Apprenticeship Learning About Multiple Intentions. ICML Monica Babes, Vukosi Marivate, Kaushik Subramanian, Michael L. Littman
2011	J	`jnl`	Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions CoRR János A. Csirik, Michael L. Littman, David A. McAllester, Robert E. Schapire, Peter Stone
2011	J	`jnl`	Democratic approximation of lexicographic preference models. Artif. Intell. Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins
2011	J	`jnl`	Integrating machine learning in Int. J. Commun. Syst. Brian Russell, Michael L. Littman, Wade Trappe
2011	J	`jnl`	Introduction to the special issue on empirical evaluations in reinforcement learning. Mach. Learn. Shimon Whiteson, Michael L. Littman
2011	J	`jnl`	Knows what it knows: a framework for self-aware learning. Mach. Learn. Lihong Li, Michael L. Littman, Thomas J. Walsh, Alexander L. Strehl
2011	A	`conf`	Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search. UAI John Asmuth, Michael L. Littman
2011	J	`jnl`	Most Relevant Explanation: computational complexity and approximation methods. Ann. Math. Artif. Intell. Changhe Yuan, Heejin Lim, Michael L. Littman
2011	J	`jnl`	On the computational complexity of stochastic controller optimization in POMDPs CoRR Nikos Vlassis, Michael L. Littman, David Barber
2011	J	`jnl`	Puzzle: baffling raffling. SIGecom Exch. Michael L. Littman, Daniel M. Reeves
2011	A*	`conf`	Sample-Based Planning for Continuous Action Markov Decision Processes. ICAPS Christopher R. Mansley, Ari Weinstein, Michael L. Littman
2011	—	`conf`	Scratchable Devices: User-Friendly Programming for Household Appliances. HCI (3) Jordan T. Ash, Monica Babes, Gal Cohen, Sameen Jalal, Sam Lichtenberg, Michael L. Littman, Vukosi Marivate, Phillip Quiza, Blase Ur, Emily Zhang
2011	A	`conf`	The effects of selection on noisy fitness optimization. GECCO Sergiu Goschin, Michael L. Littman, David H. Ackley
2011	A	`conf`	Using iterated reasoning to predict opponent strategies. AAMAS Michael Wunder, Michael Kaisers, John Robert Yaros, Michael L. Littman
2010	—	`conf`	A Cognitive Hierarchy Model Applied to the Lemonade Game. Interactive Decision Theory and Game Theory Michael Wunder, Michael L. Littman, Michael Kaisers, John Robert Yaros
2010	A	`conf`	Broadening student enthusiasm for computer science with a great insights course. SIGCSE Marie desJardins, Michael L. Littman
2010	A*	`conf`	Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration. ICML Michael Wunder, Michael L. Littman, Monica Babes
2010	J	`jnl`	Dimension reduction and its application to model-based exploration in continuous spaces. Mach. Learn. Ali Nouri, Michael L. Littman
2010	—	`conf`	Efficient Apprenticeship Learning with Smart Humans. Enabling Intelligence through Middleware Kaushik Subramanian, Michael L. Littman
2010	A*	`conf`	Generalizing Apprenticeship Learning across Hypothesis Classes. ICML Thomas J. Walsh, Kaushik Subramanian, Michael L. Littman, Carlos Diuk
2010	A*	`conf`	Integrating Sample-Based Planning and Model-Based Reinforcement Learning. AAAI Thomas J. Walsh, Sergiu Goschin, Michael L. Littman
2010	—	`ch.`	Learning Lexicographic Preference Models. Preference Learning Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins
2010	J	`jnl`	Reducing reinforcement learning to KWIK online regression. Ann. Math. Artif. Intell. Lihong Li, Michael L. Littman
2009	A	`conf`	A Bayesian Sampling Approach to Exploration in Reinforcement Learning. UAI John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate
2009	A	`conf`	Exploring compact reinforcement-learning representations with linear regression. UAI Thomas J. Walsh, Istvan Szita, Carlos Diuk, Michael L. Littman
2009	—	`ch.`	Hierarchical Reinforcement Learning. Encyclopedia of Artificial Intelligence Carlos Diuk, Michael L. Littman
2009	J	`jnl`	Learning and planning in environments with delayed feedback. Auton. Agents Multi Agent Syst. Thomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman
2009	—	`conf`	Online exploration in least-squares policy iteration. AAMAS (2) Lihong Li, Michael L. Littman, Christopher R. Mansley
2009	A*	`ed.`	Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009 ICML Andrea Pohoreckyj Danyluk, Léon Bottou, Michael L. Littman
2009	J	`jnl`	Provably Efficient Learning with Typed Parametric Models. J. Mach. Learn. Res. Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy
2009	J	`jnl`	Reinforcement Learning in Finite MDPs: PAC Analysis. J. Mach. Learn. Res. Alexander L. Strehl, Lihong Li, Michael L. Littman
2008	A	`conf`	A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games. UAI Enrique Munoz de Cote, Michael L. Littman
2008	A*	`conf`	An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning. ICML Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman
2008	J	`jnl`	An analysis of model-based Interval Estimation for Markov Decision Processes. J. Comput. Syst. Sci. Alexander L. Strehl, Michael L. Littman
2008	A*	`conf`	An object-oriented representation for efficient reinforcement learning. ICML Carlos Diuk, Andre Cohen, Michael L. Littman
2008	—	`conf`	Autonomous Model Learning for Reinforcement Learning. QEST Michael L. Littman
2008	A	`conf`	CORL: A Continuous-state Offset-dynamics Reinforcement Learner. UAI Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy
2008	A*	`conf`	Democratic approximation of lexicographic preference models. ICML Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins
2008	A*	`conf`	Efficient Learning of Action Schemas and Web-Service Descriptions. AAAI Thomas J. Walsh, Michael L. Littman
2008	Misc	`conf`	Efficient Value-Function Approximation via Online Linear Regression. ISAIM Lihong Li, Michael L. Littman
2008	A*	`conf`	Knows what it knows: a framework for self-aware learning. ICML Lihong Li, Michael L. Littman, Thomas J. Walsh
2008	—	`conf`	Multi-resolution Exploration in Continuous Spaces. NIPS Ali Nouri, Michael L. Littman
2008	J	`jnl`	Optimization problems involving collections of dependent objects. Ann. Oper. Res. David L. Roberts, Charles L. Isbell Jr., Michael L. Littman
2008	A*	`conf`	Potential-based Shaping in Model-based Reinforcement Learning. AAAI John Asmuth, Michael L. Littman, Robert Zinkov
2008	—	`conf`	Social reward shaping in the prisoner's dilemma. AAMAS (3) Monica Babes, Enrique Munoz de Cote, Michael L. Littman
2007	—	`conf`	A Multiple Representation Approach to Learning Dynamical Systems. AAAI Fall Symposium: Computational Approaches to Representation Change during Learning and Development Thomas J. Walsh, Michael L. Littman
2007	J	`jnl`	A hierarchy of prescriptive goals for multiagent learning. Artif. Intell. Martin Zinkevich, Amy Greenwald, Michael L. Littman
2007	A*	`conf`	Analyzing feature generation for value-function approximation. ICML Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman
2007	A*	`conf`	Efficient Reinforcement Learning with Relocatable Action Models. AAAI Bethany R. Leffler, Michael L. Littman, Timothy Edmunds
2007	A*	`conf`	Efficient Structure Learning in Factored-State MDPs. AAAI Alexander L. Strehl, Carlos Diuk, Michael L. Littman
2007	J	`jnl`	Introduction to the special issue on learning and computational game theory. Mach. Learn. Amy Greenwald, Michael L. Littman
2007	—	`conf`	Online Linear Regression and Its Application to Model-Based Reinforcement Learning. NIPS Alexander L. Strehl, Michael L. Littman
2007	—	`conf`	Planning and Learning in Environments with Delayed Feedback. ECML Thomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman
2006	—	`conf`	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 Carlos Diuk, Michael L. Littman
2006	A	`conf`	A hierarchical approach to efficient reinforcement learning in deterministic domains. AAMAS Carlos Diuk, Alexander L. Strehl, Michael L. Littman
2006	A	`conf`	An Efficient Optimal-Equilibrium Algorithm for Two-player Game Trees. UAI Michael L. Littman, Nishkam Ravi, Arjun Talwar, Martin Zinkevich
2006	A*	`conf`	Experience-efficient learning in associative bandit problems. ICML Alexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh
2006	A	`conf`	Incremental Model-based Learners With Formal Learning-Time Guarantees. UAI Alexander L. Strehl, Lihong Li, Michael L. Littman
2006	A*	`conf`	PAC model-free reinforcement learning. ICML Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman
2006	A*	`conf`	Targeting Specific Distributions of Trajectories in MDPs. AAAI David L. Roberts, Mark J. Nelson, Charles Lee Isbell Jr., Michael Mateas, Michael L. Littman
2006	—	`conf`	Towards a Unified Theory of State Abstraction for MDPs. AI&M Lihong Li, Thomas J. Walsh, Michael L. Littman
2005	J	`jnl`	A polynomial-time Nash equilibrium algorithm for repeated games. Decis. Support Syst. Michael L. Littman, Peter Stone
2005	A*	`conf`	A theoretical analysis of Model-Based Interval Estimation. ICML Alexander L. Strehl, Michael L. Littman
2005	A*	`conf`	Activity Recognition from Accelerometer Data. AAAI Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, Michael L. Littman
2005	J	`jnl`	Combining Independent Modules in Lexical Multiple-Choice Problems CoRR Peter D. Turney, Michael L. Littman, Jeffrey Bigham, Victor Shnayder
2005	J	`jnl`	Corpus-based Learning of Analogies and Semantic Relations CoRR Peter D. Turney, Michael L. Littman
2005	J	`jnl`	Corpus-based Learning of Analogies and Semantic Relations. Mach. Learn. Peter D. Turney, Michael L. Littman
2005	—	`conf`	Cyclic Equilibria in Markov Games. NIPS Martin Zinkevich, Amy Greenwald, Michael L. Littman
2005	—	`conf`	Efficient Exploration With Latent Structure. Robotics: Science and Systems Bethany R. Leffler, Michael L. Littman, Alexander L. Strehl, Thomas J. Walsh
2005	A*	`conf`	Lazy Approximation for Solving Continuous Finite-Horizon MDPs. AAAI Lihong Li, Michael L. Littman
2005	J	`jnl`	Reports on the 2004 AAAI Fall Symposia. AI Mag. 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 Singh, Shlomo Argamon, Shlomo Dubnov
2005	J	`jnl`	The First Probabilistic Track of the International Planning Competition. J. Artif. Intell. Res. Håkan L. S. Younes, Michael L. Littman, David Weissman, John Asmuth
2004	B	`conf`	An Empirical Evaluation of Interval Estimation for Markov Decision Processes. ICTAI Alexander L. Strehl, Michael L. Littman
2004	A*	`conf`	An Instance-Based State Representation for Network Repair. AAAI Michael L. Littman, Nishkam Ravi, Eitan Fenson, Richard E. Howard
2004	C	`conf`	Planning with predictive state representations. ICMLA Michael R. James, Satinder Singh, Michael L. Littman
2004	—	`conf`	Reinforcement Learning for Autonomic Network Repair. ICAC Michael L. Littman, Nishkam Ravi, Eitan Fenson, Richard E. Howard
2003	A*	`conf`	A polynomial-time nash equilibrium algorithm for repeated games. EC Michael L. Littman, Peter Stone
2003	J	`jnl`	AAAI-2002 Fall Symposium Series. AI Mag. Yukio Ohsawa, Peter McBurney, Simon Parsons, Christopher A. Miller, Alan C. Schultz, Jean Scholtz, Michael Goodrich, Eugene Santos Jr., Benjamin Bell, Charles Lee Isbell Jr., Michael L. Littman
2003	J	`jnl`	Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems CoRR Peter D. Turney, Michael L. Littman, Jeffrey Bigham, Victor Shnayder
2003	Misc	`conf`	Combining independent modules in lexical multiple-choice problems. RANLP Peter D. Turney, Michael L. Littman, Jeffrey Bigham, Victor Shnayder
2003	J	`jnl`	Contingent planning under uncertainty via stochastic satisfiability. Artif. Intell. Stephen M. Majercik, Michael L. Littman
2003	J	`jnl`	Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. J. Artif. Intell. Res. Peter Stone, Robert E. Schapire, Michael L. Littman, János A. Csirik, David A. McAllester
2003	J	`jnl`	Learning Analogies and Semantic Relations CoRR Peter D. Turney, Michael L. Littman
2003	A*	`conf`	Learning Predictive State Representations. ICML Satinder Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, Peter Stone
2003	J	`jnl`	Measuring Praise and Criticism: Inference of Semantic Orientation from Association CoRR Peter D. Turney, Michael L. Littman
2003	J	`jnl`	Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans. Inf. Syst. Peter D. Turney, Michael L. Littman
2003	A*	`conf`	Tutorial: Learning Topics in Game-Theoretic Decision Making. COLT Michael L. Littman
2002	J	`jnl`	A probabilistic approach to solving crossword puzzles. Artif. Intell. Michael L. Littman, Greg A. Keim, Noam M. Shazeer
2002	—	`conf`	ATTac-2001: A Learning, Autonomous Bidding Agent. AMEC Peter Stone, Robert E. Schapire, János A. Csirik, Michael L. Littman, David A. McAllester
2002	—	`conf`	Least-Squares Methods in Reinforcement Learning for Control. SETN Michail G. Lagoudakis, Ronald Parr, Michael L. Littman
2002	A*	`conf`	Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. ICML Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik
2002	A	`conf`	Randomized strategic demand reduction: getting more by asking for less. AAMAS Paul S. A. Reitsma, Peter Stone, János A. Csirik, Michael L. Littman
2002	—	`conf`	Self-Enforcing Strategic Demand Reduction. AMEC Paul S. A. Reitsma, Peter Stone, János A. Csirik, Michael L. Littman
2002	J	`jnl`	Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus CoRR Peter D. Turney, Michael L. Littman
2001	J	`jnl`	ATTac-2000: An Adaptive Autonomous Bidding Agent. J. Artif. Intell. Res. Peter Stone, Michael L. Littman, Satinder Singh, Michael J. Kearns
2001	—	`conf`	ATTac-2000: an adaptive autonomous bidding agent. Agents Peter Stone, Michael L. Littman, Satinder Singh, Michael J. Kearns
2001	—	`conf`	An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. NIPS Michael L. Littman, Michael J. Kearns, Satinder Singh
2001	—	`conf`	Approximate Dimension Reduction at NTCIR. NTCIR Fan Jiang, Michael L. Littman
2001	—	`conf`	FAucS : An FCC Spectrum Auction Simulator for Autonomous Bidding Agents. WELCOM János A. Csirik, Michael L. Littman, Satinder Singh, Peter Stone
2001	A*	`conf`	Friend-or-Foe Q-learning in General-Sum Games. ICML Michael L. Littman
2001	A	`conf`	Graphical Models for Game Theory. UAI Michael J. Kearns, Michael L. Littman, Satinder Singh
2001	—	`conf`	Implicit Negotiation in Repeated Games. ATAL Michael L. Littman, Peter Stone
2001	J	`jnl`	Learning to Select Branching Rules in the DPLL Procedure for Satisfiability. Electron. Notes Discret. Math. Michail G. Lagoudakis, Michael L. Littman
2001	—	`conf`	PAC Generalization Bounds for Co-training. NIPS Sanjoy Dasgupta, Michael L. Littman, David A. McAllester
2001	—	`conf`	Predictive Representations of State. NIPS Michael L. Littman, Richard S. Sutton, Satinder Singh
2001	J	`jnl`	Stochastic Boolean Satisfiability. J. Autom. Reason. Michael L. Littman, Stephen M. Majercik, Toniann Pitassi
2001	J	`jnl`	Value-function reinforcement learning in Markov games. Cogn. Syst. Res. Michael L. Littman
2000	J	`jnl`	A Review of Reinforcement Learning. AI Mag. Sebastian Thrun, Michael L. Littman
2000	—	`conf`	Abstraction Methods for Game Theoretic Poker. Computers and Games Jiefu Shi, Michael L. Littman
2000	A*	`conf`	Algorithm Selection using Reinforcement Learning. ICML Michail G. Lagoudakis, Michael L. Littman
2000	A*	`conf`	Approximate Dimension Equalization in Vector-based Information Retrieval. ICML Fan Jiang, Michael L. Littman
2000	J	`jnl`	Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms. Mach. Learn. Satinder Singh, Tommi S. Jaakkola, Michael L. Littman, Csaba Szepesvári
2000	—	`conf`	Exact Solutions to Time-Dependent MDPs. NIPS Justin A. Boyan, Michael L. Littman
2000	—	`conf`	Reinforcement Learning for Algorithm Selection. AAAI/IAAI Michail G. Lagoudakis, Michael L. Littman
2000	—	`conf`	Review: Computer Language Games. Computers and Games Michael L. Littman
2000	—	`conf`	Towards Approximately Optimal Poker. AAAI/IAAI Jiefu Shi, Michael L. Littman
1999	J	`jnl`	A Unified Analysis of Value-Function-Based Reinforcement Learning Algorithms. Neural Comput. Csaba Szepesvári, Michael L. Littman
1999	—	`conf`	Contingent Planning Under Uncertainty via Stochastic Satisfiability. AAAI/IAAI Stephen M. Majercik, Michael L. Littman
1999	—	`conf`	Initial Experiments in Stochastic Satisfiability. AAAI/IAAI Michael L. Littman
1999	—	`conf`	PROVERB: The Probabilistic Cruciverbalist. AAAI/IAAI 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	—	`conf`	Solving Crossword Puzzles as Probabilistic Constraint Satisfaction. AAAI/IAAI Noam M. Shazeer, Michael L. Littman, Greg A. Keim
1999	—	`conf`	Solving Crosswords with PROVERB. AAAI/IAAI Michael L. Littman, Greg A. Keim, Noam M. Shazeer
1999	J	`jnl`	The AAAI Fall Symposia. AI Mag. Giuseppe De Giacomo, Marie desJardins, Dolores Cañamero, Glenn S. Wasson, Michael L. Littman, Gerard Allwein, Kim Marriott, Bernd Meyer, Barbara Webb, Tom Con
1998	A*	`conf`	Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus. ICML Michael L. Littman, Fan Jiang, Greg A. Keim
1998	—	`conf`	MAXPLAN: A New Approach to Probabilistic Planning. AIPS Stephen M. Majercik, Michael L. Littman
1998	J	`jnl`	Planning and Acting in Partially Observable Stochastic Domains. Artif. Intell. Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra
1998	J	`jnl`	The Computational Complexity of Probabilistic Planning CoRR Michael L. Littman, Judy Goldsmith, Martin Mundhenk
1998	J	`jnl`	The Computational Complexity of Probabilistic Planning. J. Artif. Intell. Res. Michael L. Littman, Judy Goldsmith, Martin Mundhenk
1998	—	`conf`	Using Caching to Solve Larger Probabilistic Planning Problems. AAAI/IAAI Stephen M. Majercik, Michael L. Littman
1997	—	`conf`	Automatic 3-Language Cross-Language Information Retrieval with Latent Semantic Indexing. TREC Bob Rehder, Michael L. Littman, Susan T. Dumais, Thomas K. Landauer
1997	A	`conf`	Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes. UAI Anthony R. Cassandra, Michael L. Littman, Nevin Lianwen Zhang
1997	—	`conf`	Probabilistic Propositional Planning: Representations and Complexity. AAAI/IAAI Michael L. Littman
1997	—	`conf`	Speeding Safely: Multi-Criteria Optimization in Probabilistic Planning. AAAI/IAAI Michael S. Fulkerson, Michael L. Littman, Greg A. Keim
1997	A	`conf`	The Complexity of Plan Existence and Evaluation in Probabilistic Domains. UAI Judy Goldsmith, Michael L. Littman, Martin Mundhenk
1996	A*	`conf`	A Generalized Reinforcement-Learning Model: Convergence and Applications. ICML Michael L. Littman, Csaba Szepesvári
1996	J	`jnl`	Reinforcement Learning: A Survey CoRR Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore
1996	J	`jnl`	Reinforcement Learning: A Survey. J. Artif. Intell. Res. Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore
1996	J	`jnl`	Taggers for Parsers. Artif. Intell. Eugene Charniak, Glenn Carroll, John E. Adcock, Anthony R. Cassandra, Yoshihiko Gotoh, Jeremy Katz, Michael L. Littman, John McCann
1995	A*	`conf`	Learning Policies for Partially Observable Environments: Scaling Up. ICML Michael L. Littman, Anthony R. Cassandra, Leslie Pack Kaelbling
1995	A	`conf`	On the Complexity of Solving Markov Decision Problems. UAI Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling
1995	Misc	`conf`	Partially Observable Markov Decision Processes for Artificial Intelligence. KI Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra
1995	—	`conf`	Partially Observable Markov Decision Processes for Artificial Intelligence. Reasoning with Uncertainty in Robotics Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra
1994	A*	`conf`	Acting Optimally in Partially Observable Stochastic Domains. AAAI Anthony R. Cassandra, Leslie Pack Kaelbling, Michael L. Littman
1994	A*	`conf`	Markov Games as a Framework for Multi-Agent Reinforcement Learning. ICML Michael L. Littman
1993	—	`conf`	An interface for navigating clustered document sets returned by queries. COOCS Robert B. Allen, Pascal Obry, Michael L. Littman
1993	—	`conf`	Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach. NIPS Justin A. Boyan, Michael L. Littman
1992	A	`conf`	Supporting Informal Communication via Ephemeral Interest Groups. CSCW Laurence Brothers, James D. Hollan, Jakob Neilsen, Scott Stornetta, Steven P. Abney, George W. Furnas, Michael L. Littman
1991	—	`conf`	Adaptation in Constant Utility Non-Stationary Environments. ICGA Michael L. Littman, David H. Ackley
1991	—	`conf`	Hypertext for the Electronic Library? CORE Sample Results. Hypertext Dennis E. Egan, Michael Lesk, R. Daniel Ketchum, Carol C. Lochbaum, Joel R. Remde, Michael L. Littman, Thomas K. Landauer
1989	—	`conf`	Generalization and Scaling in Reinforcement Learning. NIPS David H. Ackley, Michael L. Littman