M. Littman

发表

The Witness Algorithm: Solving Partially Observable Markov Decision Processes

M. Littman, 1994 .

Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search pdf

Michael L. Littman, John Asmuth, M. Littman, 2011, UAI.

Exploring compact reinforcement-learning representations with linear regression

Thomas J. Walsh, Michael L. Littman, Carlos Diuk, 2009, UAI.

An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning

Lihong Li, Michael L. Littman, Gavin Taylor, 2008, ICML '08.

Markov Games as a Framework for Multi-Agent Reinforcement Learning

Michael L. Littman, M. Littman, 1994, ICML.

Sample-Based Planning for Continuous Action Markov Decision Processes

Michael L. Littman, Ari Weinstein, Christopher R. Mansley, 2011, ICAPS.

Learning Policies for Partially Observable Environments: Scaling Up

Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra, 1997, ICML.

Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms

Csaba Szepesv Ari, M. Littman, 1996 .

Efficient Reinforcement Learning with Relocatable Action Models

Michael L. Littman, Bethany R. Leffler, Timothy Edmunds, 2007, AAAI.

Memoryless policies: theoretical limitations and practical results

Michael L. Littman, M. Littman, 1994 .

Reinforcement Learning: A Survey

Andrew W. Moore, Leslie Pack Kaelbling, Michael L. Littman, 1996, J. Artif. Intell. Res..

PPDDL 1 . 0 : An Extension to PDDL for Expressing Planning Domains with Probabilistic Effects

Håkan L. S. Younes, M. Littman, 2004 .

Efficient dynamic-programming updates in partially observable Markov decision processes

M. Littman, A. Cassandra, L. Kaelbling, 1995 .

Efficient dynamic-programming updates in partially observable Markov decision processes

M. Littman, A. Cassandra, L. Kaelbling, 1995 .

Planning and Acting in Partially Observable Stochastic Domains

Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra, 1998, Artif. Intell..

A Novel Benchmark Methodology and Data Repository for Real-life Reinforcement Learning

Ronald E. Parr, M. Littman, Lihong Li, 2009 .

The First Probabilistic Track of the International Planning Competition

Håkan L. S. Younes, Michael L. Littman, John Asmuth, 2005, J. Artif. Intell. Res..

An object-oriented representation for efficient reinforcement learning

Andre Cohen, Carlos Diuk, Michael L Littman, 2008, ICML '08.

Approaching Bayes-optimalilty using Monte-Carlo tree search

M. Littman, J. Asmuth, 2011 .

An Efficient Exact Algorithm for Singly Connected Graphical Games

Satinder Singh, M. Littman, M. Kearns, 2002, NIPS 2002.

Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach

Michael L. Littman, Justin A. Boyan, M. Littman, 1993, NIPS.

PAC model-free reinforcement learning

Lihong Li, Michael L. Littman, Alexander L. Strehl, 2006, ICML.

An Introduction to Reinforcement Learning

Andrew W. Moore, Leslie Pack Kaelbling, Michael L. Littman, 1995 .

Algorithms for Sequential Decision Making

Michael L. Littman, M. Littman, 1996 .

MAXPLAN: A New Approach to Probabilistic Planning

Michael L. Littman, Stephen M. Majercik, M. Littman, 1998, AIPS.

Acting Optimally in Partially Observable Stochastic Domains

Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra, 1994, AAAI.

Least-Squares Methods in Reinforcement Learning for Control

Michail G. Lagoudakis, Michael L. Littman, Ronald Parr, 2002, SETN.

A Generalized Reinforcement-Learning Model: Convergence and Applications

Csaba Szepesvári, Michael L. Littman, Csaba Szepesvari, 1996, ICML.

Predictive Representations of State

Richard S. Sutton, Michael L. Littman, Satinder P. Singh, 2001, NIPS.

A Distributed Reinforcement Learning Scheme for Network Routing

Michael L. Littman, Justin A. Boyan, M. Littman, 1993 .

Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes

Michael L. Littman, Nevin Lianwen Zhang, Anthony R. Cassandra, 1997, UAI.

Potential-based Shaping in Model-based Reinforcement Learning

Michael L. Littman, Robert Zinkov, John Asmuth, 2008, AAAI.

Solving for Best Responses in Extensive-Form Games using Reinforcement Learning Methods

M. Littman, A. Greenwald, Jiacui Li, 2013 .

Knows what it knows: a framework for self-aware learning

Thomas J. Walsh, Lihong Li, Michael L. Littman, 2008, ICML '08.

An analysis of model-based Interval Estimation for Markov Decision Processes

Michael L. Littman, Alexander L. Strehl, M. Littman, 2008, J. Comput. Syst. Sci..

Confidence Bands for Roc Curves

Sofus A. Macskassy, M. Littman, F. Provost, 2003 .

PAC Generalization Bounds for Co-training

Sanjoy Dasgupta, David A. McAllester, Michael L. Littman, 2001, NIPS.

An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games

Michael L. Littman, Michael Kearns, Satinder P. Singh, 2001, NIPS.

A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms

Csaba Szepesvári, Michael L. Littman, Csaba Szepesvari, 1999, Neural Computation.

Deep Radial-Basis Value Functions for Continuous Control

Kavosh Asadi, Michael L. Littman, Neev Parikh, 2021, AAAI.

Towards a Unified Theory of State Abstraction for MDPs

Thomas J. Walsh, Lihong Li, Michael L. Littman, 2006, AI&M.

Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms

Tommi S. Jaakkola, Csaba Szepesvári, Michael L. Littman, 2000, Machine Learning.

Learning Predictive State Representations

Peter Stone, Michael L. Littman, Satinder P. Singh, 2003, ICML.

Efficient Learning of Dynamics Models using Terrain Classification

Bethany R. Leffler, M. Littman, C. Mansley, 2008 .

An optimization-based categorization of reinforcement learning environments

Michael L. Littman, M. Littman, 1993 .

Generalization and Scaling in Reinforcement Learning

David H. Ackley, Michael L. Littman, M. Littman, 1989, NIPS.

robabilistic Propositional Planning : Representations and Complexity

M. Littman, 1999 .

XGvis: Interactive Data Visualization with Multidimensional Scaling

Michael L. Littman, Nathaniel Dean, Andreas Buja, 1998 .

Interactive Learning from Policy-Dependent Human Feedback pdf

Guan Wang, Matthew E. Taylor, Michael L. Littman, 2017, ICML.

Dimension reduction and its application to model-based exploration in continuous spaces

Michael L. Littman, Ali Nouri, M. Littman, 2010, Machine Learning.

On the Complexity of Solving Markov Decision Problems pdf

Leslie Pack Kaelbling, Thomas L. Dean, Michael L. Littman, 1995, UAI.

A Bayesian Sampling Approach to Exploration in Reinforcement Learning pdf

Lihong Li, Michael L. Littman, David Wingate, 2009, UAI.

Interactions between learning and evolution

David H. Ackley, Michael L. Littman, M. Littman, 1991 .

Graphical Models for Game Theory

Michael L. Littman, Michael Kearns, Satinder P. Singh, 2001, UAI.

Online Linear Regression and Its Application to Model-Based Reinforcement Learning

Michael L. Littman, Alexander L. Strehl, M. Littman, 2007, NIPS.

Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning

David L. Roberts, Matthew E. Taylor, Michael L. Littman, 2015, Autonomous Agents and Multi-Agent Systems.

Training an Agent to Ground Commands with Reward and Punishment

David L. Roberts, Matthew E. Taylor, Michael L. Littman, 2014, AAAI 2014.

Data Visualization With Multidimensional Scaling

Deborah F. Swayne, M. Littman, A. Buja, 2008 .

The 2006 AAAI Computer Poker Competition

Michael L. Littman, Martin Zinkevich, M. Littman, 2006 .

Initial Experiments in Probabilistic Satissability Initial Experiments in Probabilistic Satissability

M. Littman, 2007 .

Apprenticeship Learning About Multiple Intentions

Michael L. Littman, Kaushik Subramanian, Vukosi N. Marivate, 2011, ICML.

The Computational Complexity of Probabilistic Planning

Michael L. Littman, Judy Goldsmith, Martin Mundhenk, 1998, J. Artif. Intell. Res..

Integrating Sample-Based Planning and Model-Based Reinforcement Learning

Thomas J. Walsh, Michael L. Littman, Sergiu Goschin, 2010, AAAI.

Probabilistic Propositional Planning: Representations and Complexity

Michael L. Littman, M. Littman, 1997, AAAI/IAAI.

Value-function reinforcement learning in Markov games

Michael L. Littman, M. Littman, 2001, Cognitive Systems Research.

The Complexity of Plan Existence and Evaluation in Probabilistic Domains pdf

Michael L. Littman, Judy Goldsmith, Martin Mundhenk, 1997, UAI.

Practical trigger-action programming in the smart home

Blase Ur, Michael L. Littman, Melwyn Pak Yong Ho, 2014, CHI.

Toward Good Abstractions for Lifelong Learning

M. Littman, David Abel, Dilip Arumugam, 2017 .

Stochastic Boolean Satisfiability

Toniann Pitassi, Michael L. Littman, Stephen M. Majercik, 2001, Journal of Automated Reasoning.

Applying prerequisite structure inference to adaptive testing

Evan Cater, Michael L. Littman, Sam Saarinen, 2020, LAK.

Planning in Reward-Rich Domains via PAC Bandits

Michael L. Littman, Sergiu Goschin, Ari Weinstein, 2012, EWRL.

Matrix computations for query expansion in information retrieval

Michael L. Littman, Fan Jiang, M. Littman, 2000 .

A Comparison of Two Corpus-Based Methods for Translingual Information Retrieval

M. Littman, Fan Jiang, 2000 .

Randomized strategic demand reduction: getting more by asking for less

Peter Stone, Michael L. Littman, Paul S. A. Reitsma, 2002, AAMAS '02.

Solving Crossword Puzzles as Probabilistic Constraint Satisfaction

Michael L. Littman, Greg A. Keim, Noam M. Shazeer, 1999, AAAI/IAAI.

Learning Analogies and Semantic Relations

Michael L. Littman, Peter D. Turney, M. Littman, 2003, ArXiv.

Learning Lexicographic Preference Models

Thomas J. Walsh, Marie desJardins, Michael L. Littman, 2010, Preference Learning.

Contingent planning under uncertainty via stochastic satisfiability

Michael L. Littman, Stephen M. Majercik, M. Littman, 1999, Artif. Intell..

Stochastic Boolean Satis ability

M. Littman, S. Majercik, 2022 .

Corpus-based Learning of Analogies and Semantic Relations

Michael L. Littman, Peter D. Turney, M. Littman, 2005, Machine Learning.

Bandit-Based Solar Panel Control

Michael L. Littman, David Abel, Emily Reif, 2018, AAAI.

Toward Improving Solar Panel Efficiency using Reinforcement Learning

M. Littman, Emily Reif, David Abel, 2017 .

Improving Solar Panel Efficiency Using Reinforcement Learning

M. Littman, Emily Reif, David Abel, 2017 .

Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus

Fan Jiang, Michael L. Littman, Greg A. Keim, 1998, ICML.

Mean Actor Critic

M. Littman, Abdel-rahman Mohamed, G. Konidaris, 2017, ArXiv.

Teaching by Intervention: Working Backwards, Undoing Mistakes, or Correcting Mistakes?

Michael L. Littman, Joseph L. Austerweil, Mark K. Ho, 2017, CogSci.

Large-Scale Planning Under Uncertainty : A Survey

M. Littman, S. Majercik, 1997 .

Measuring praise and criticism: Inference of semantic orientation from association

Michael L. Littman, Peter D. Turney, M. Littman, 2003, TOIS.

Selecting the Right Algorithm

Michail G. Lagoudakis, Michael L. Littman, Ronald Parr, 2001 .

Friend-or-Foe Q-learning in General-Sum Games

Michael L. Littman, M. Littman, 2001, ICML.

A polynomial-time nash equilibrium algorithm for repeated games

Peter Stone, Michael L. Littman, M. Littman, 2003, EC '03.

Showing versus doing: Teaching by demonstration

Fiery Cushman, Michael L. Littman, Joseph L. Austerweil, 2016, NIPS.

Context-Driven Satirical News Generation

Michael L. Littman, Zachary Horvitz, Nam Do, 2020, Fig-Lang@ACL.

Reward-predictive representations generalize across tasks in reinforcement learning

Michael L. Littman, Michael J. Frank, Lucas Lehnert, 2019, bioRxiv.

Ask Me Anything about MOOCs

Todd W. Neller, Michael L. Littman, Charles Lee Isbell, 2017, AI Mag..

An Empirical Study of Non-Expert Curriculum Design for Machine Learners

M. Littman, J. MacGlashan, 2016 .

Reducing reinforcement learning to KWIK online regression

Lihong Li, Michael L. Littman, M. Littman, 2010, Annals of Mathematics and Artificial Intelligence.

Visualizing the embedding of objects in Euclidean space

Michael L. Littman, Nathaniel Dean, Andreas Buja, 1992 .

An Instance-Based State Representation for Network Repair

Michael L. Littman, Richard E. Howard, Eitan Fenson, 2004, AAAI.

Exploration in Least-Squares Policy Iteration

M. Littman, Lihong Li, C. Mansley, 2008 .

Adaptation in Constant Utility Non-Stationary Environments

David H. Ackley, Michael L. Littman, M. Littman, 1991, ICGA.

A unifying framework for computational reinforcement learning theory

Michael L. Littman, Lihong Li, M. Littman, 2009 .

Targeting Specific Distributions of Trajectories in MDPs

David L. Roberts, Michael Mateas, Michael L. Littman, 2006, AAAI.

A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games

Michael L. Littman, Enrique Munoz de Cote, M. Littman, 2008, UAI.

Between Imitation and Intention Learning

Michael L. Littman, James MacGlashan, M. Littman, 2015, IJCAI.

Altruism in the evolution of communication

David H. Ackley, Michael L. Littman, M. Littman, 1994 .

Social is special: A normative framework for teaching with and learning from evaluative feedback

Mark K. Ho, M. Littman, J. MacGlashan, 2017, Cognition.

A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback

David L. Roberts, Matthew E. Taylor, Michael L. Littman, 2014, AAAI.

Reinforcement Learning for Algorithm Selection

Michail G. Lagoudakis, Michael L. Littman, M. Littman, 2000, AAAI/IAAI.

Exploration via Model-based Interval Estimation

M. Littman, A. Strehl, 2004 .

ATTac-2000: an adaptive autonomous bidding agent

Peter Stone, Michael L. Littman, Satinder P. Singh, 2001, AGENTS '01.

ATTac-2001: A Learning, Autonomous Bidding Agent

Peter Stone, David A. McAllester, Robert E. Schapire, 2002, AMEC.

Perception-based generalization in model-based reinforcement learning

Michael L. Littman, Bethany R. Leffler, M. Littman, 2009 .

Feature-based Joint Planning and Norm Learning in Collaborative Games

Joshua B. Tenenbaum, Michael L. Littman, Joseph L. Austerweil, 2016, CogSci.

FAucS : An FCC Spectrum Auction Simulator for Autonomous Bidding Agents

Peter Stone, Michael L. Littman, Satinder P. Singh, 2001, WELCOM.

Who speaks for AI?

Eric Eaton, Thomas G. Dietterich, Michael Wooldridge, 2016, SIGAI.

Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation

Peter Stone, David A. McAllester, Robert E. Schapire, 2002, ICML.

Near Optimal Behavior via Approximate State Abstraction

Michael L. Littman, David Abel, D. Ellis Hershkowitz, 2016, ICML.

A polynomial-time nash equilibrium algorithm for repeated games

Peter Stone, Michael L. Littman, M. Littman, 2003, EC '03.

Modeling Latent Attention Within Neural Networks

Lawson L. S. Wong, Christopher Grimm, Michael L. Littman, 2018 .

Q-learning in Two-Player Two-Action Games

M. Littman, M. Wunder, M. Babes, 2009 .

Coordinate to cooperate or compete: Abstract goals and joint intentions in social interaction

Joshua B. Tenenbaum, Michael L. Littman, Joseph L. Austerweil, 2016, CogSci.

Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration

Michael L. Littman, Monica Babes-Vroman, Michael Wunder, 2010, ICML.

Automatic Cross-Language Information Retrieval Using Latent Semantic Indexing

Susan T. Dumais, Michael L. Littman, Thomas K. Landauer, 1998 .

Trigger-Action Programming in the Wild: An Analysis of 200,000 IFTTT Recipes

Jiyun Lee, Blase Ur, Michael L. Littman, 2016, CHI.

Democratic approximation of lexicographic preference models

Thomas J. Walsh, Marie desJardins, Michael L. Littman, 2008, ICML '08.

Open-Loop Planning in Large-Scale Stochastic Domains

Michael L. Littman, Ari Weinstein, M. Littman, 2013, AAAI.

Language and Policy Learning from Human-delivered Feedback

Matthew E. Taylor, M. Littman, J. MacGlashan, 2015 .

Leading Best-Response Strategies in Repeated Games

Peter Stone, Michael L. Littman, M. Littman, 2001, International Joint Conference on Artificial Intelligence.

Exact Solutions to Time-Dependent MDPs

Michael L. Littman, Justin A. Boyan, M. Littman, 2000, NIPS.

Hypertext for the electronic library?: CORE sample results

Michael E. Lesk, Michael L. Littman, Dennis E. Egan, 1991, HYPERTEXT '91.

State Abstraction as Compression in Apprenticeship Learning

Lawson L. S. Wong, Kavosh Asadi, Michael L. Littman, 2019, AAAI.

Effectively Learning from Pedagogical Demonstrations

Fiery Cushman, Michael L. Littman, Joseph L. Austerweil, 2018, CogSci.

Using iterated reasoning to predict opponent strategies

Michael L. Littman, Michael Kaisers, Michael Wunder, 2011, AAMAS.

PAC Reinforcement Learning in Noisy Continuous Worlds

Bethany R. Leffler, M. Littman, Emma Brunskill, 2008 .

PAC-MDP Reinforcement Learning with Bayesian Priors

Michael L. Littman, David Wingate, Ali Nouri, 2009 .

SKILL DISCOVERY WITH WELL-DEFINED OBJECTIVES

M. Littman, G. Konidaris, David Abel, 2019 .

“ Structure & Priors in Reinforcement Learning ” at ICLR 2019 V ALUE P RESERVING S TATE-A CTION A BSTRACTIONS ( A PPENDIX )

Doina Precup, M. Littman, Khimya Khetarpal, 2019 .

People Teach With Rewards and Punishments as Communication, Not Reinforcements

Joseph L. Austerweil, Mark K. Ho, M. Littman, 2018, Journal of experimental psychology. General.

Finding Optimal Strategies over Families of Tasks in Reinforcement Learning

M. Littman, G. Konidaris, Yuu Jinnai, 2018 .

Data augmentation and the role of hardness for feature learning in NLP

M. Littman, Ellie Pavlick, Rohan Jha, 2020 .

Generalization in Deep Reinforcement Learning

M. Littman, David D. Jensen, Emma Tosch, 2018 .

Algorithm Selection using Reinforcement Learning

Michail G. Lagoudakis, Michael L. Littman, M. Littman, 2000, ICML.

IPC-4 Probabilistic Planning Track : FAQ 1 . 01 November 1 , 2003

Håkan L. S. Younes, M. Littman, 2003 .

An Alternative Softmax Operator for Reinforcement Learning

Kavosh Asadi, Michael L. Littman, M. Littman, 2016, ICML.

The Impact of Other-Regarding Preferences in a Collection of Non-Zero-Sum Grid Games

Joseph L. Austerweil, Mark K. Ho, M. Littman, 2016 .

Cyclic Equilibria in Markov Games

Michael L. Littman, Martin Zinkevich, Amy Greenwald, 2005, NIPS.

The Efficiency of Human Cognition Reflects Planned Information Processing

Mark K. Ho, Jonathan D. Cohen, M. Littman, 2020, AAAI Conference on Artificial Intelligence.

Finding Options that Minimize Planning Time (Appendix)

M. Littman, G. Konidaris, D. E. Hershkowitz, 2019 .

Lazy Approximation : A New Approach for Solving Continuous Finite-Horizon MDPs

M. Littman, Lihong Li, 2005 .

Cost-Sensitive Fault Remediation for Autonomic Computing

Thu D. Nguyen, M. Littman, H. Hirsh, 2003 .

Reinforcement Learning in Finite MDPs : PAC Analysis Reinforcement Learning in Finite MDPs : PAC Analysis

M. Littman, A. Strehl, 2008 .

Autonomous Quadrotor Control with Reinforcement Learning

M. Littman, C. Mansley, Michael C. Koval, 2010 .

Introduction to the Probabilistic Planning Track

Håkan L. S. Younes, M. Littman, 2004 .

Bayesian Adaptive Sampling for Variable Selection and Model Averaging

Michael L. Littman, Merlise A. Clyde, Joyee Ghosh, 2011 .

An Empirical Analysis of RL's Drift From Its Behaviorism Roots

Matthew E. Taylor, M. Littman, R. Loftin, 2012 .

Interactive Data Visualization with Multidimensional Scaling

Deborah F. Swayne, M. Littman, A. Buja, 2022 .

Stochastic POMDP controllers: How easy to optimize?

David Barber, Michael L. Littman, Nikos Vlassis, 2012 .

Evolution of flexibility and rigidity in retaliatory punishment

M. Littman, J. MacGlashan, F. Cushman, 2017, Proceedings of the National Academy of Sciences.

Structure & Priors in Reinforcement Learning (SPiRL)

Thomas L. Griffiths, Junhyuk Oh, Abhishek Gupta, 2019, ICLR 2019.

Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009

Michael L. Littman, Léon Bottou, Andrea Danyluk, 2009, International Conference on Machine Learning.

Proceedings, Twenty-Sixth International Conference on Machine Learning

Michael L. Littman, Léon Bottou, L. Bottou, 2009 .

Taggers for Parsers Taggers for Parsers 1

M. Littman, A. Cassandra, Eugene Charniak, 1996 .

Constraint Satisfaction withProbabilistic Preferences on Variable

M. Littman, ValuesNoam M. Shazeer, Greg A. KeimDepartment, 1999 .

IPC-4 Probabilistic Planning Track : FAQ 1 . 0 September 2003

Håkan L. S. Younes, M. Littman, 2003 .

IPC-4 Probabilistic Planning Track : FAQ 0 . 5 September 13 , 2003

Håkan L. S. Younes, M. Littman, 2003 .

Inferring the Intentions of Learning Agents

M. Littman, A. Greenwald, Vincent Kubala, 2018 .

Approximate Planning in the Probabilistic-Planning-as-Stochastic-Satisfiability Paradigm

M. Littman, S. Majercik, 2006 .

People construct simplified mental representations to plan.

Mark K. Ho, Jonathan D. Cohen, M. Littman, 2022, Nature.

Maxibook: User-centered Hypertext on an Ascii Terminal

M. Littman, J. Remde, 2007 .

Designing Rewards for Fast Learning

M. Littman, Zhi-Hua Zhou, Henry Sowerby, 2022, ArXiv.

ATTac-2000: An Adaptive Autonomous Bidding Agent

Satinder Singh, M. Littman, P. Stone, 2001, J. Artif. Intell. Res..

The Computational Complexity of Probabilistic Planning

M. Littman, J. Goldsmith, M. Mundhenk, 1998, J. Artif. Intell. Res..

Simple Learning in Moded Non-stationary Environments

M. Littman, Sushant Agarwal, 2022 .

Automatic cross-linguistic information retrieval using latent semantic indexing

Susan T. Dumais, Michael L. Littman, Thomas K. Landauer, 2007 .

Efficient dynamic-programming updates in partially observable Markov decision processes

M. Littman, A. Cassandra, L. Kaelbling, 1995 .

Explaining Why: How Instructions and User Interfaces Impact Annotator Rationales When Labeling Text Data

Jamar L. Sullivan, M. Littman, Yuxin Chen, 2022, North American Chapter of the Association for Computational Linguistics.

Generalized Markov Decision Processes : Dynamic-programming and Reinforcement-learningAlgorithmsCsaba Szepesv

M. Littman, 2008 .

Optimal planning to plan: People partially plan based on plan specificity

Thomas L. Griffiths, Jonathan D. Cohen, Michael L. Littman, 2019, 2019 Conference on Cognitive Computational Neuroscience.

A Bibliography of Work Related to Reinforcement Learning

Leslie Pack Kaelbling, Michael L. Littman, M. Littman, 1994 .

An Efficient Algorithm for Dynamic Programming in Partially Observable Markov Decision Processes

Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra, 1995 .

Simulations combining evolution and learning

Michael L. Littman, M. Littman, 1996 .

Automatic 3-language cross-language information retrieval with LSA

Michael L. Littman, Thomas K. Landauer, Robert Rehder, 1997 .

Speeding Safely: Multi-Criteria Optimization in Probabilistic Planning

Michael L. Littman, Greg A. Keim, Michael S. Fulkerson, 1997, AAAI/IAAI.

Algorithms for Partially Observable Markov Decision Processes

Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra, 1994 .

On the Expressivity of Markov Reward (Extended Abstract)

Mark K. Ho, Doina Precup, M. Littman, 2022, International Joint Conference on Artificial Intelligence.

Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report

Morgan E. Currie, Michael C. Horowitz, Gillian K. Hadfield, 2022, ArXiv.

Comparing Global with Disease specific Machine-learned Readmission Prediction Models

M. Littman, I. Sarkar, L. Mercurio, 2020 .

Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex

M. Littman, G. Konidaris, Ellie Pavlick, 2022, NeurIPS.

People construct simplified mental representations to plan

Mark K. Ho, Jonathan D. Cohen, Carlos G. Correa, 2021, Nature.

Task Scoping: Generating Task-Specific Abstractions for Planning

M. Littman, G. Konidaris, Stefanie Tellex, 2020, 2010.08869.

Reward-Predictive Clustering

Michael J. Frank, M. Littman, Lucas Lehnert, 2022, ArXiv.

XGvis : Intera tive Data Visualizationwith Multidimensional S alingAndreas

Deborah F. Swayne, M. Littman, N. Dean, 2001 .

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Santhosh K. Ramakrishnan, Gautam K. Vallabha, Praveen K. Pilly, 2023, Neural Networks.

Deep RBF Value Functions for Continuous Control pdf

Kavosh Asadi, Michael L. Littman, Ronald E. Parr, 2020, ArXiv.

Theory of Minds: Understanding Behavior in Groups Through Inverse Planning

Joshua B. Tenenbaum, Michael L. Littman, Max Kleiman-Weiner, 2019, AAAI.

Coco-Q: Learning in Stochastic Games with Side Payments

Michael L. Littman, Amy Greenwald, Eric Sodomka, 2013, ICML.

Ìì Blockin¹¾¼¼¼¼ Ò Ôøøú Ùøóòóñóù× Òò Òø

Satinder Singh, M. Littman, P. Stone, 2007 .

Representation and Learning in Computational Game Theory

M. Littman, M. Kearns, Michael Kearns, 2003 .

Control of mental representations in human planning pdf

Thomas L. Griffiths, Michael L. Littman, Mark K. Ho, 2021, ArXiv.

Efficient model-based exploration in continuous state-space environments

Michael L. Littman, Ali Nouri, M. Littman, 2011 .

Reinforcement Learning in Finite MDPs: PAC Analysis

Lihong Li, Michael L. Littman, Alexander L. Strehl, 2009, J. Mach. Learn. Res..

Incremental Model-based Learners With Formal Learning-Time Guarantees pdf

Lihong Li, Michael L. Littman, Alexander L. Strehl, 2006, UAI.

Policy and Value Transfer in Lifelong Reinforcement Learning

Michael L. Littman, George Konidaris, David Abel, 2018, ICML.

On the Computational Complexity of Stochastic Controller Optimization in POMDPs

David Barber, Michael L. Littman, Nikos Vlassis, 2011, TOCT.

Reinforcement Learning as a Framework for Ethical Decision Making

Michael L. Littman, James MacGlashan, David Abel, 2016, AAAI Workshop: AI, Ethics, and Society.

Trace2TAP: Synthesizing Trigger-Action Programs from Traces of Behavior

M. Littman, Shan Lu, Blase Ur, 2020, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

Trace2TAP: Synthesizing Trigger-Action Programs from Traces of Behavior

Blase Ur, Michael L. Littman, Lefan Zhang, 2020, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

Curriculum Design for Machine Learners in Sequential Decision Tasks

Matthew E. Taylor, Michael L. Littman, Bei Peng, 2017, IEEE Transactions on Emerging Topics in Computational Intelligence.

A tutorial on partially observable Markov decision processes

Michael L. Littman, M. Littman, 2009 .

Efficient learning of relational models for sequential decision making

Thomas J. Walsh, Michael L. Littman, M. Littman, 2010 .

Activity Recognition from Accelerometer Data

Michael L. Littman, Nishkam Ravi, Nikhil Dandekar, 2005, AAAI.

Value Preserving State-Action Abstractions

Doina Precup, Khimya Khetarpal, Michael L. Littman, 2020, AISTATS.

An empirical evaluation of interval estimation for Markov decision processes

Michael L. Littman, Alexander L. Strehl, M. Littman, 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.

A probabilistic approach to solving crossword puzzles

Michael L. Littman, Greg A. Keim, Noam M. Shazeer, 2002, Artif. Intell..

Review: Computer Language Games

Michael L. Littman, M. Littman, 2000, Computers and Games.

Environment-Independent Task Specifications via GLTL pdf

Ufuk Topcu, Jie Fu, Min Wen, 2017, ArXiv.

Abstraction Methods for Game Theoretic Poker

Michael L. Littman, Jiefu Shi, M. Littman, 2000, Computers and Games.

Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions pdf

David A. McAllester, P. Stone, J. A. Csirik, 2003, J. Artif. Intell. Res..

Adaptive dynamic server placement in MANETs

R. Chadha, A. Poylisher, B. Deb, 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

Just-in-time and just-in-place deadlock resolution

Michael L. Littman, Fancong Zeng, M. Littman, 2007 .

Reinforcement learning improves behaviour from evaluative feedback

Michael L. Littman, M. Littman, 2015, Nature.

A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans

David L. Roberts, Matthew E. Taylor, Michael L. Littman, 2016, AAMAS.

On the Expressivity of Markov Reward pdf

Doina Precup, Michael L. Littman, Mark K. Ho, 2021, NeurIPS.

Implicit Negotiation in Repeated Games

Peter Stone, Michael L. Littman, M. Littman, 2001, ATAL.

Model Selection's Disparate Impact in Real-World Deep Learning Applications pdf

Michael Littman, Jessica Zosa Forde, Chris De Sa, 2021, ArXiv.

Efficient Structure Learning in Factored-State MDPs

Michael L. Littman, Alexander L. Strehl, Carlos Diuk, 2007, AAAI.

Learning State Abstractions for Transfer in Continuous Control pdf

Kavosh Asadi, Michael L. Littman, David Abel, 2020, ArXiv.

State Abstractions for Lifelong Reinforcement Learning

Michael L. Littman, Dilip Arumugam, David Abel, 2018, ICML.

Partially Observable Markov Decision Processes for Artificial Intelligence

Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra, 1995, Reasoning with Uncertainty in Robotics.

Communication in action: Planning and interpreting communicative demonstrations.

Joseph L. Austerweil, Mark K. Ho, M. Littman, 2021, Journal of experimental psychology. General.

Inducing Partially Observable Markov Decision Processes

Michael L. Littman, M. Littman, 2012, ICGI.

Covering Number as a Complexity Measure for POMDP Planning and Learning

Xiaoping Chen, Michael L. Littman, Zongzhang Zhang, 2012, AAAI.

Communication, Credibility and Negotiation Using a Cognitive Hierarchy Model

Matthew Stone, Michael L. Littman, Michael Wunder, 2009 .

Learning and planning in environments with delayed feedback

Thomas J. Walsh, Lihong Li, Michael L. Littman, 2009, Autonomous Agents and Multi-Agent Systems.

Algorithms for Informed Cows

Ming-Yang Kao, Michael L. Littman, M. Littman, 1997 .

Lipschitz Continuity in Model-based Reinforcement Learning

Kavosh Asadi, Dipendra Misra, Michael L. Littman, 2018, ICML.

Automatic 3-Language Cross-Language Information Retrieval with Latent Semantic Indexing

Susan T. Dumais, Bob Rehder, Michael L. Littman, 1997, TREC.

Broadening student enthusiasm for computer science with a great insights course

Marie desJardins, Michael L. Littman, M. Littman, 2010, SIGCSE.

Marie desJardins, Michael L. Littman, John Barr, 2019, Inroads.

Collusion rings threaten the integrity of computer science research

Michael L. Littman, M. Littman, 2021, Commun. ACM.

The effects of selection on noisy fitness optimization

David H. Ackley, Michael L. Littman, Sergiu Goschin, 2011, GECCO '11.

Finding Options that Minimize Planning Time

Michael L. Littman, George Konidaris, David Abel, 2018, ICML.

The value of abstraction

Mark K. Ho, David Abel, Thomas L Griffiths, 2019, Current Opinion in Behavioral Sciences.

Automatic Cross-Language Retrieval Using Latent Semantic Indexing

Michael L. Littman, Thomas K. Landauer, Susan T. Dumais, 1997 .

Combining independent modules in lexical multiple-choice problems pdf

Jeffrey P. Bigham, Michael L. Littman, Jeffrey Bigham, 2004, RANLP.

Deep Reinforcement Learning from Policy-Dependent Human Feedback pdf

Michael L. Littman, Jun Ki Lee, Dilip Arumugam, 2019, ArXiv.

Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning

Michael L. Littman, Lucas Lehnert, M. Littman, 2019, J. Mach. Learn. Res..

Measuring and Characterizing Generalization in Deep Reinforcement Learning pdf

Michael L. Littman, Jun Ki Lee, Emma Tosch, 2018, Applied AI Letters.

DeepMellow: Removing the Need for a Target Network in Deep Q-Learning

Kavosh Asadi, George Konidaris, Seungchan Kim, 2019, IJCAI.

Proximal Iteration for Deep Reinforcement Learning

Alexander J. Smola, M. Littman, Kavosh Asadi, 2022 .

Planning with Abstract Markov Decision Processes

Stefanie Tellex, Lawson L. S. Wong, Marie desJardins, 2017, ICAPS.

Analyzing feature generation for value-function approximation

Lihong Li, Michael L. Littman, Ronald Parr, 2007, ICML '07.

Lipschitz Lifelong Reinforcement Learning pdf

Erwan Lecarpentier, Kavosh Asadi, Michael L. Littman, 2020, AAAI.

Learning-based route management in wireless ad hoc networks

Michael L. Littman, Brian Russell, M. Littman, 2008 .

Understanding Trigger-Action Programs Through Novel Visualizations of Program Differences

Shan Lu, Blase Ur, Michael L. Littman, 2021, CHI.

Bandit-Based Planning and Learning in Continuous-Action Markov Decision Processes

Michael L. Littman, Ari Weinstein, M. Littman, 2012, ICAPS.

Rollout-based Game-tree Search Outprunes Traditional Alpha-beta

Michael L. Littman, Sergiu Goschin, Ari Weinstein, 2012, EWRL.

A hierarchical approach to efficient reinforcement learning in deterministic domains

Michael L. Littman, Alexander L. Strehl, Carlos Diuk, 2006, AAMAS '06.

Grounding English Commands to Reward Functions

Smaranda Muresan, Stefanie Tellex, Marie desJardins, 2015, Robotics: Science and Systems.

Teaching with Rewards and Punishments: Reinforcement or Communication?

Fiery Cushman, Michael L. Littman, Joseph L. Austerweil, 2015, CogSci.

People Teach with Rewards and Punishments as Communication not Reinforcements

Joseph L. Austerweil, Mark K. Ho, M. Littman, 2018 .

Most Relevant Explanation: computational complexity and approximation methods

Changhe Yuan, Michael L. Littman, Heejin Lim, 2011, Annals of Mathematics and Artificial Intelligence.

Planning and Learning in Environments with Delayed Feedback

Thomas J. Walsh, Lihong Li, Michael L. Littman, 2007, ECML.

Towards a Simple Approach to Multi-step Model-based Reinforcement Learning pdf

Kavosh Asadi, Evan Cater, Dipendra Misra, 2018, ArXiv.

Efficient Learning of Action Schemas and Web-Service Descriptions

Thomas J. Walsh, Michael L. Littman, M. Littman, 2008, AAAI.

Faster Deep Reinforcement Learning with Slower Online Network

Alexander J. Smola, M. Littman, Kavosh Asadi, 2021, NeurIPS.

Deep Q-Network with Proximal Iteration pdf

Alexander J. Smola, Kavosh Asadi, Michael L. Littman, 2021, ArXiv.

Lazy Approximation for Solving Continuous Finite-Horizon MDPs

Lihong Li, Michael L. Littman, M. Littman, 2005, AAAI.

Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus

Michael L. Littman, Peter D. Turney, M. Littman, 2002, ArXiv.

Initial Progress Toward Development of a Voice-Based Computer-Delivered Motivational Intervention for Heavy Drinking College Students: An Experimental Study

Tyler B. Wray, M. Littman, J. MacGlashan, 2017, JMIR mental health.

Learning to Select Branching Rules in the DPLL Procedure for Satisfiability

Michail G. Lagoudakis, Michael L. Littman, M. Littman, 2001, Electron. Notes Discret. Math..

Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning pdf

Stefanie Tellex, Michael L. Littman, Lucas Lehnert, 2017, ArXiv.

Convergent Actor Critic by Humans

David L. Roberts, Matthew E. Taylor, Michael L. Littman, 2016 .

Teaching Agents with Evaluative Feedback: Communication versus Reward

M. Littman, 2015 .

Equivalence Between Wasserstein and Value-Aware Model-based Reinforcement Learning

Kavosh Asadi, Evan Cater, Dipendra Misra, 2018, ArXiv.

Equivalence Between Wasserstein and Value-Aware Model-based Reinforcement Learning pdf

Kavosh Asadi, Evan Cater, Dipendra Misra, 2018, ArXiv.

Social reward shaping in the prisoner's dilemma

Michael L. Littman, Enrique Munoz de Cote, Monica Babes-Vroman, 2008, AAMAS.

Partially Observable Markov Decision Processes for Artificial Intelligence

M. Littman, A. Cassandra, L. Kaelbling, 1995, Reasoning with Uncertainty in Robotics.

State Abstractions for Lifelong Reinforcement Learning

M. Littman, David Abel, Dilip Arumugam, 2018, ICML.

Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems

Jeffrey P. Bigham, Michael L. Littman, Jeffrey Bigham, 2003, ArXiv.

Combating the Compounding-Error Problem with a Multi-step Model pdf

Kavosh Asadi, Dipendra Misra, Seungchan Kim, 2019, ArXiv.

The Expected-Length Model of Options

Marie desJardins, Michael L. Littman, John Winder, 2019, IJCAI.

Stackelberg Punishment and Bully-Proofing Autonomous Vehicles pdf

Michael L. Littman, Matt Cooper, Jun Ki Lee, 2019, ICSR.

Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program pdf

Michael L. Littman, Jeff Druce, James Niehaus, 2021, ArXiv.

An Efficient Optimal-Equilibrium Algorithm for Two-player Game Trees pdf

Michael L. Littman, Martin Zinkevich, Nishkam Ravi, 2006, UAI.

An Ensemble of Linearly Combined Reinforcement-Learning Agents

Michael L. Littman, Vukosi N. Marivate, M. Littman, 2013, AAAI.

A New Softmax Operator for Reinforcement Learning pdf

Kavosh Asadi, Michael L. Littman, M. Littman, 2016, ArXiv.

A framework for modeling population strategies by depth of reasoning

Michael L. Littman, Michael Kaisers, Michael Wunder, 2012, AAMAS.

Specifying Behavior Preference with Tiered Reward Functions

M. Littman, Henry Sowerby, Zhiyuan Zhou, 2022, arXiv.org.

A theoretical analysis of Model-Based Interval Estimation

Michael L. Littman, Alexander L. Strehl, M. Littman, 2005, ICML.

Experience-efficient learning in associative bandit problems

Chris Mesterharm, Haym Hirsh, Michael L. Littman, 2006, ICML.

The Efficiency of Human Cognition Reflects Planned Information Processing pdf

Thomas L. Griffiths, Michael L. Littman, Mark K. Ho, 2020, AAAI 2020.

Interactive Learning of Environment Dynamics for Sequential Tasks pdf

David L. Roberts, Matthew E. Taylor, Michael L. Littman, 2019, ArXiv.

Mitigating Planner Overfitting in Model-Based Reinforcement Learning pdf

Christopher Grimm, Kavosh Asadi, Michael L. Littman, 2018, ArXiv.

Multi-resolution Exploration in Continuous Spaces

Michael L. Littman, Ali Nouri, M. Littman, 2008, NIPS.

Learning something from nothing: Leveraging implicit human feedback strategies

David L. Roberts, Matthew E. Taylor, Michael L. Littman, 2014, The 23rd IEEE International Symposium on Robot and Human Interactive Communication.

Successor Features Support Model-based and Model-free Reinforcement Learning pdf

Michael L. Littman, Lucas Lehnert, M. Littman, 2019, ArXiv.

Transfer with Model Features in Reinforcement Learning pdf

Michael L. Littman, Lucas Lehnert, M. Littman, 2018, ArXiv.

Teaching a Robot Tasks of Arbitrary Complexity via Human Feedback

Michael L. Littman, Mark K. Ho, Carl Trimbach, 2020, 2020 15th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

Teaching with IMPACT pdf

Michael L. Littman, Carl Trimbach, M. Littman, 2019, ArXiv.

Reinforcement Learning as a Framework for Ethical Decision Making

M. Littman, J. MacGlashan, David Abel, 2016, AAAI Workshop: AI, Ethics, and Society.

A Review of Reinforcement Learning

Sebastian Thrun, Michael L. Littman, M. Littman, 2000, AI Mag..

What can we do?

M. Littman, J. Hawks, 2001, Urologic nursing.

PROVERB: The Probabilistic Cruciverbalist

Karl Weinmeister, Fan Jiang, Michael L. Littman, 1999, AAAI/IAAI.

Initial Experiments in Stochastic Satisfiability

Michael L. Littman, M. Littman, 1999, AAAI/IAAI.

An interface for navigating clustered document sets returned by queries

Robert B. Allen, Michael L. Littman, Pascal Obry, 1993, COCS '93.

Efficient Learning of Action Schemas and Web-Service Descriptions

M. Littman, Thomas J. Walsh, 2008, AAAI.

Supporting informal communication via ephemeral interest groups

James D. Hollan, Scott Stornetta, George W. Furnas, 1992, CSCW '92.

Evidence Humans Provide When Explaining Data-Labeling Decisions

Blase Ur, Bowen Wang, Michael L. Littman, 2019, INTERACT.

Online exploration in least-squares policy iteration

Lihong Li, Michael L. Littman, Christopher R. Mansley, 2009, AAMAS.

Bad-Policy Density: A Measure of Reinforcement Learning Hardness pdf

Lawson L. S. Wong, Michael L. Littman, Dilip Arumugam, 2021, ArXiv.

A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback

Matthew E. Taylor, M. Littman, Jeff Huang, 2014, AAAI.

Planning with predictive state representations

Michael L. Littman, Michael R. James, Satinder P. Singh, 2004, 2004 International Conference on Machine Learning and Applications, 2004. Proceedings..

Where, When & Which Concepts Does AlphaZero Learn? Lessons from the Game of Hex

M. Littman, G. Konidaris, Ellie Pavlick, 2022 .

Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging

M. Littman, J. Zech, J. Forde, 2019, ArXiv.

How Users Interpret Bugs in Trigger-Action Programming

Blase Ur, Guan Wang, Michael L. Littman, 2019, CHI.

Implicit Negotiation in Repeated Games

M. Littman, P. Stone, 2001, ATAL.

Self-Enforcing Strategic Demand Reduction

Peter Stone, Michael L. Littman, Paul S. A. Reitsma, 2002, AMEC.

Quantifying Uncertainty in Batch Personalized Sequential Decision Making

Michael L. Littman, Emma Brunskill, Vukosi N. Marivate, 2014, AAAI Workshop: Modern Artificial Intelligence for Health Analytics.

Automatic Cross-Language Information Retrieval Using Latent Semantic Indexing

M. Littman, T. Landauer, Susan T. Dumais, 1998 .

Automatic Cross-Language Retrieval Using Latent Semantic Indexing

Todd A. Letsche, M. Littman, T. Landauer, 1997 .

Markov Decision Processes

Michael L. Littman, M. Littman, 2001 .

Disambiguation by community membership

Michael L. Littman, M. Littman, Richard J. Gerrig, 1990, Memory & cognition.

Near Optimal Behavior via Approximate State Abstraction

M. Littman, David Abel, D. E. Hershkowitz, 2016, ICML.

Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus

Peter D. Turney, M. Littman, 2002, ArXiv.

Integrating machine learning in ad hoc routing: A wireless adaptive routing protocol

Wade Trappe, Michael L. Littman, Brian Russell, 2011, Int. J. Commun. Syst..

Mnemotechnics in Second-Language Learning

Jane S. Vogler, Thomas C. Toppino, Melva R. Grant, 2012 .

A statistical method for language-independent representation of the topical content of text segments

Michael L. Littman, Thomas K. Landauer, M. Littman, 2007 .