Michael L. Littman
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
114
Venues
25
Active years
1992–2026
Best venue rank
A*
Where they publish
- A*AAAI36 papers
- A*ICML29 papers
- AUAI11 papers
- BCogSci6 papers
- A*CHI5 papers
- A*IJCAI5 papers
- ASIGCSE2 papers
- A*HRI2 papers
- NationalISAIM2 papers
- ANAACL1 paper
- AAISTATS1 paper
- ALAK1 paper
- BInteract1 paper
- ARecSys1 paper
- BRO-MAN1 paper
- AAAMAS1 paper
- AGECCO1 paper
- NationalHCI1 paper
- CICMLA1 paper
- BICTAI1 paper
- A*COLT1 paper
- NationalRANLP1 paper
- NationalKI1 paper
- BGroup1 paper
- ACSCW1 paper
Papers
114 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | SIGCSE | Learning Persistence & Resistance from History & SIGCSE Reads. | Rebecca Bates, Judy Goldsmith, Valerie Summet, Nanette Veilleux, Katie Johnson, Michael L. Littman, Kyla A. McMullen, Jeremy A. Magruder Waisome |
| 2025 | CHI | How Humans Communicate Programming Tasks in Natural Language and Implications For End-User Programming with LLMs. | Madison Pickering, Helena Williams, Alison Gan, Weijia He, Hyojae Park, Francisco Piedrahita Velez, Michael L. Littman, Blase Ur |
| 2025 | HRI | Enabling End Users to Program Robots Using Reinforcement Learning. | Tewodros W. Ayalew, Jennifer Wang, Michael L. Littman, Blase Ur, Sarah Sebo |
| 2022 | IJCAI | On the Expressivity of Markov Reward (Extended Abstract). | David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh |
| 2022 | IJCAI | On the (In)Tractability of Reinforcement Learning for LTL Objectives. | Cambridge Yang, Michael L. Littman, Michael Carbin |
| 2022 | NAACL | Explaining Why: How Instructions and User Interfaces Impact Annotator Rationales When Labeling Text Data. | Jamar L. Sullivan Jr., William Brackenbury, Andrew McNut, Kevin Bryson, Kwam Byll, Yuxin Chen, Michael L. Littman, Chenhao Tan, Blase Ur |
| 2021 | AAAI | Deep Radial-Basis Value Functions for Continuous Control. | Kavosh Asadi, Neev Parikh, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman |
| 2021 | AAAI | Lipschitz Lifelong Reinforcement Learning. | Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman |
| 2021 | AAAI | Towards Sample Efficient Agents through Algorithmic Alignment (Student Abstract). | Mingxuan Li, Michael L. Littman |
| 2021 | CHI | Understanding Trigger-Action Programs Through Novel Visualizations of Program Differences. | Valerie Zhao, Lefan Zhang, Bo Wang, Michael L. Littman, Shan Lu, Blase Ur |
| 2020 | AAAI | People Do Not Just Plan, They Plan to Plan. | Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths |
| 2020 | AISTATS | Value Preserving State-Action Abstractions. | David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman |
| 2020 | HRI | Teaching a Robot Tasks of Arbitrary Complexity via Human Feedback. | Guan Wang, Carl Trimbach, Jun Ki Lee, Mark K. Ho, Michael L. Littman |
| 2020 | LAK | Applying prerequisite structure inference to adaptive testing. | Sam Saarinen, Evan Cater, Michael L. Littman |
| 2019 | AAAI | State Abstraction as Compression in Apprenticeship Learning. | David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong |
| 2019 | AAAI | Theory of Minds: Understanding Behavior in Groups through Inverse Planning. | Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum |
| 2019 | CHI | How Users Interpret Bugs in Trigger-Action Programming. | Will Brackenbury, Abhimanyu Deora, Jillian Ritchey, Jason Vallee, Weijia He, Guan Wang, Michael L. Littman, Blase Ur |
| 2019 | ICML | Finding Options that Minimize Planning Time. | Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris |
| 2019 | IJCAI | The Expected-Length Model of Options. | David Abel, John Winder, Marie desJardins, Michael L. Littman |
| 2019 | IJCAI | DeepMellow: Removing the Need for a Target Network in Deep Q-Learning. | Seungchan Kim, Kavosh Asadi, Michael L. Littman, George Dimitri Konidaris |
| 2019 | Interact | Evidence Humans Provide When Explaining Data-Labeling Decisions. | Judah Newman, Bowen Wang, Valerie Zhao, Amy Zeng, Michael L. Littman, Blase Ur |
| 2018 | AAAI | Bandit-Based Solar Panel Control. | David Abel, Edward C. Williams, Stephen Brawner, Emily Reif, Michael L. Littman |
| 2018 | CogSci | Effectively Learning from Pedagogical Demonstrations. | Mark K. Ho, Michael L. Littman, Fiery Cushman, Joseph L. Austerweil |
| 2018 | ICML | State Abstractions for Lifelong Reinforcement Learning. | David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman |
| 2018 | ICML | Policy and Value Transfer in Lifelong Reinforcement Learning. | David Abel, Yuu Jinnai, Yue (Sophie) Guo, George Dimitri Konidaris, Michael L. Littman |
| 2018 | ICML | Lipschitz Continuity in Model-based Reinforcement Learning. | Kavosh Asadi, Dipendra Misra, Michael L. Littman |
| 2017 | CogSci | Teaching by Intervention: Working Backwards, Undoing Mistakes, or Correcting Mistakes? | Mark K. Ho, Michael L. Littman, Joseph L. Austerweil |
| 2017 | ICML | An Alternative Softmax Operator for Reinforcement Learning. | Kavosh Asadi, Michael L. Littman |
| 2017 | ICML | Interactive Learning from Policy-Dependent Human Feedback. | James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman |
| 2016 | AAAI | Reinforcement Learning as a Framework for Ethical Decision Making. | David Abel, James MacGlashan, Michael L. Littman |
| 2016 | CHI | Trigger-Action Programming in the Wild: An Analysis of 200, 000 IFTTT Recipes. | Blase Ur, Melwyn Pak Yong Ho, Stephen Brawner, Jiyun Lee, Sarah Mennicken, Noah Picard, Diane Schulze, Michael L. Littman |
| 2016 | CogSci | Feature-based Joint Planning and Norm Learning in Collaborative Games. | 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 | CogSci | Coordinate to cooperate or compete: Abstract goals and joint intentions in social interaction. | Max Kleiman-Weiner, Mark K. Ho, Joseph L. Austerweil, Michael L. Littman, Josh Tenenbaum |
| 2016 | ICML | Near Optimal Behavior via Approximate State Abstraction. | David Abel, D. Ellis Hershkowitz, Michael L. Littman |
| 2016 | RecSys | Learning User's Preferred Household Organization via Collaborative Filtering Methods. | Stephen Brawner, Michael L. Littman |
| 2015 | CogSci | Teaching with Rewards and Punishments: Reinforcement or Communication? | Mark K. Ho, Michael L. Littman, Fiery Cushman, Joseph L. Austerweil |
| 2015 | IJCAI | Between Imitation and Intention Learning. | James MacGlashan, Michael L. Littman |
| 2014 | AAAI | A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback. | Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, Jeff Huang, David L. Roberts |
| 2014 | AAAI | Quantifying Uncertainty in Batch Personalized Sequential Decision Making. | Vukosi Marivate, Jessica Chemali, Emma Brunskill, Michael L. Littman |
| 2014 | CHI | Practical trigger-action programming in the smart home. | Blase Ur, Elyse McManus, Melwyn Pak Yong Ho, Michael L. Littman |
| 2014 | CogSci | Flexible theft and resolute punishment: Evolutionary dynamics of social behavior among reinforcement-learning agents. | James MacGlashan, Michael L. Littman, Fiery Cushman |
| 2014 | RO-MAN | Learning something from nothing: Leveraging implicit human feedback strategies. | Robert Tyler Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts |
| 2013 | AAAI | AAAI-13 Preface. | Marie desJardins, Michael L. Littman |
| 2013 | AAAI | An Ensemble of Linearly Combined Reinforcement-Learning Agents. | Vukosi Marivate, Michael L. Littman |
| 2013 | AAAI | Open-Loop Planning in Large-Scale Stochastic Domains. | Ari Weinstein, Michael L. Littman |
| 2013 | ICML | The Cross-Entropy Method Optimizes for Quantiles. | Sergiu Goschin, Ari Weinstein, Michael L. Littman |
| 2013 | ICML | Coco-Q: Learning in Stochastic Games with Side Payments. | Eric Sodomka, Elizabeth Hilliard, Michael L. Littman, Amy Greenwald |
| 2012 | AAAI | Covering Number as a Complexity Measure for POMDP Planning and Learning. | Zongzhang Zhang, Michael L. Littman, Xiaoping Chen |
| 2012 | AAMAS | A framework for modeling population strategies by depth of reasoning. | Michael Wunder, John Robert Yaros, Michael Kaisers, Michael L. Littman |
| 2011 | GECCO | The effects of selection on noisy fitness optimization. | Sergiu Goschin, Michael L. Littman, David H. Ackley |
| 2011 | HCI | Scratchable Devices: User-Friendly Programming for Household Appliances. | Jordan T. Ash, Monica Babes, Gal Cohen, Sameen Jalal, Sam Lichtenberg, Michael L. Littman, Vukosi Marivate, Phillip Quiza, Blase Ur, Emily Zhang |
| 2011 | ICML | Apprenticeship Learning About Multiple Intentions. | Monica Babes, Vukosi Marivate, Kaushik Subramanian, Michael L. Littman |
| 2011 | UAI | Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search. | John Asmuth, Michael L. Littman |
| 2010 | AAAI | Efficient Apprenticeship Learning with Smart Humans. | Kaushik Subramanian, Michael L. Littman |
| 2010 | AAAI | Integrating Sample-Based Planning and Model-Based Reinforcement Learning. | Thomas J. Walsh, Sergiu Goschin, Michael L. Littman |
| 2010 | AAAI | A Cognitive Hierarchy Model Applied to the Lemonade Game. | Michael Wunder, Michael L. Littman, Michael Kaisers, John Robert Yaros |
| 2010 | ICML | Generalizing Apprenticeship Learning across Hypothesis Classes. | Thomas J. Walsh, Kaushik Subramanian, Michael L. Littman, Carlos Diuk |
| 2010 | ICML | Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration. | Michael Wunder, Michael L. Littman, Monica Babes |
| 2010 | SIGCSE | Broadening student enthusiasm for computer science with a great insights course. | Marie desJardins, Michael L. Littman |
| 2009 | UAI | A Bayesian Sampling Approach to Exploration in Reinforcement Learning. | John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate |
| 2009 | UAI | Exploring compact reinforcement-learning representations with linear regression. | Thomas J. Walsh, Istvan Szita, Carlos Diuk, Michael L. Littman |
| 2008 | AAAI | Potential-based Shaping in Model-based Reinforcement Learning. | John Asmuth, Michael L. Littman, Robert Zinkov |
| 2008 | AAAI | Efficient Learning of Action Schemas and Web-Service Descriptions. | Thomas J. Walsh, Michael L. Littman |
| 2008 | ICML | An object-oriented representation for efficient reinforcement learning. | Carlos Diuk, Andre Cohen, Michael L. Littman |
| 2008 | ICML | Knows what it knows: a framework for self-aware learning. | Lihong Li, Michael L. Littman, Thomas J. Walsh |
| 2008 | ICML | An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning. | Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman |
| 2008 | ICML | Democratic approximation of lexicographic preference models. | Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins |
| 2008 | ISAIM | Efficient Value-Function Approximation via Online Linear Regression. | Lihong Li, Michael L. Littman |
| 2008 | UAI | CORL: A Continuous-state Offset-dynamics Reinforcement Learner. | Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy |
| 2008 | UAI | A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games. | Enrique Munoz de Cote, Michael L. Littman |
| 2007 | AAAI | Efficient Reinforcement Learning with Relocatable Action Models. | Bethany R. Leffler, Michael L. Littman, Timothy Edmunds |
| 2007 | AAAI | Efficient Structure Learning in Factored-State MDPs. | Alexander L. Strehl, Carlos Diuk, Michael L. Littman |
| 2007 | ICML | Analyzing feature generation for value-function approximation. | Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman |
| 2006 | AAAI | Targeting Specific Distributions of Trajectories in MDPs. | David L. Roberts, Mark J. Nelson, Charles Lee Isbell Jr., Michael Mateas, Michael L. Littman |
| 2006 | ICML | PAC model-free reinforcement learning. | Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman |
| 2006 | ICML | Experience-efficient learning in associative bandit problems. | Alexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh |
| 2006 | ISAIM | Towards a Unified Theory of State Abstraction for MDPs. | Lihong Li, Thomas J. Walsh, Michael L. Littman |
| 2006 | UAI | An Efficient Optimal-Equilibrium Algorithm for Two-player Game Trees. | Michael L. Littman, Nishkam Ravi, Arjun Talwar, Martin Zinkevich |
| 2006 | UAI | Incremental Model-based Learners With Formal Learning-Time Guarantees. | Alexander L. Strehl, Lihong Li, Michael L. Littman |
| 2005 | AAAI | Lazy Approximation for Solving Continuous Finite-Horizon MDPs. | Lihong Li, Michael L. Littman |
| 2005 | AAAI | Activity Recognition from Accelerometer Data. | Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, Michael L. Littman |
| 2005 | ICML | A theoretical analysis of Model-Based Interval Estimation. | Alexander L. Strehl, Michael L. Littman |
| 2004 | AAAI | An Instance-Based State Representation for Network Repair. | Michael L. Littman, Nishkam Ravi, Eitan Fenson, Richard E. Howard |
| 2004 | ICMLA | Planning with predictive state representations. | Michael R. James, Satinder Singh, Michael L. Littman |
| 2004 | ICTAI | An Empirical Evaluation of Interval Estimation for Markov Decision Processes. | Alexander L. Strehl, Michael L. Littman |
| 2003 | COLT | Tutorial: Learning Topics in Game-Theoretic Decision Making. | Michael L. Littman |
| 2003 | ICML | Learning Predictive State Representations. | Satinder Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, Peter Stone |
| 2003 | RANLP | Combining independent modules in lexical multiple-choice problems. | Peter D. Turney, Michael L. Littman, Jeffrey Bigham, Victor Shnayder |
| 2002 | ICML | Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. | Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Jnos A. Csirik |
| 2001 | ICML | Friend-or-Foe Q-learning in General-Sum Games. | Michael L. Littman |
| 2001 | UAI | Graphical Models for Game Theory. | Michael J. Kearns, Michael L. Littman, Satinder Singh |
| 2000 | AAAI | Reinforcement Learning for Algorithm Selection. | Michail G. Lagoudakis, Michael L. Littman |
| 2000 | AAAI | Towards Approximately Optimal Poker. | Jiefu Shi, Michael L. Littman |
| 2000 | ICML | Approximate Dimension Equalization in Vector-based Information Retrieval. | Fan Jiang, Michael L. Littman |
| 2000 | ICML | Algorithm Selection using Reinforcement Learning. | Michail G. Lagoudakis, Michael L. Littman |
| 1999 | AAAI | PROVERB: The Probabilistic Cruciverbalist. | 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 | AAAI | Initial Experiments in Stochastic Satisfiability. | Michael L. Littman |
| 1999 | AAAI | Solving Crosswords with PROVERB. | Michael L. Littman, Greg A. Keim, Noam M. Shazeer |
| 1999 | AAAI | Contingent Planning Under Uncertainty via Stochastic Satisfiability. | Stephen M. Majercik, Michael L. Littman |
| 1999 | AAAI | Solving Crossword Puzzles as Probabilistic Constraint Satisfaction. | Noam M. Shazeer, Michael L. Littman, Greg A. Keim |
| 1998 | AAAI | Using Caching to Solve Larger Probabilistic Planning Problems. | Stephen M. Majercik, Michael L. Littman |
| 1998 | ICML | Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus. | Michael L. Littman, Fan Jiang, Greg A. Keim |
| 1997 | AAAI | Speeding Safely: Multi-Criteria Optimization in Probabilistic Planning. | Michael S. Fulkerson, Michael L. Littman, Greg A. Keim |
| 1997 | AAAI | Probabilistic Propositional Planning: Representations and Complexity. | Michael L. Littman |
| 1997 | UAI | Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes. | Anthony R. Cassandra, Michael L. Littman, Nevin Lianwen Zhang |
| 1997 | UAI | The Complexity of Plan Existence and Evaluation in Probabilistic Domains. | Judy Goldsmith, Michael L. Littman, Martin Mundhenk |
| 1996 | ICML | A Generalized Reinforcement-Learning Model: Convergence and Applications. | Michael L. Littman, Csaba Szepesvri |
| 1995 | ICML | Learning Policies for Partially Observable Environments: Scaling Up. | Michael L. Littman, Anthony R. Cassandra, Leslie Pack Kaelbling |
| 1995 | KI | Partially Observable Markov Decision Processes for Artificial Intelligence. | Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra |
| 1995 | UAI | On the Complexity of Solving Markov Decision Problems. | Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling |
| 1994 | AAAI | Acting Optimally in Partially Observable Stochastic Domains. | Anthony R. Cassandra, Leslie Pack Kaelbling, Michael L. Littman |
| 1994 | ICML | Markov Games as a Framework for Multi-Agent Reinforcement Learning. | Michael L. Littman |
| 1993 | Group | An interface for navigating clustered document sets returned by queries. | Robert B. Allen, Pascal Obry, Michael L. Littman |
| 1992 | CSCW | Supporting Informal Communication via Ephemeral Interest Groups. | Laurence Brothers, James D. Hollan, Jakob Neilsen, Scott Stornetta, Steven P. Abney, George W. Furnas, Michael L. Littman |