| 2008 |
|---|
| 112 | | John Asmuth,
Michael L. Littman,
Robert Zinkov:
Potential-based Shaping in Model-based Reinforcement Learning.
AAAI 2008: 604-609 |
| 111 | | Thomas J. Walsh,
Michael L. Littman:
Efficient Learning of Action Schemas and Web-Service Descriptions.
AAAI 2008: 714-719 |
| 110 | EE | Monica Babes,
Enrique Munoz de Cote,
Michael L. Littman:
Social reward shaping in the prisoner's dilemma.
AAMAS (3) 2008: 1389-1392 |
| 109 | EE | Fusun Yaman,
Thomas J. Walsh,
Michael L. Littman,
Marie desJardins:
Democratic approximation of lexicographic preference models.
ICML 2008: 1200-1207 |
| 108 | EE | Carlos Diuk,
Andre Cohen,
Michael L. Littman:
An object-oriented representation for efficient reinforcement learning.
ICML 2008: 240-247 |
| 107 | EE | Lihong Li,
Michael L. Littman,
Thomas J. Walsh:
Knows what it knows: a framework for self-aware learning.
ICML 2008: 568-575 |
| 106 | EE | Ronald Parr,
Lihong Li,
Gavin Taylor,
Christopher Painter-Wakefield,
Michael L. Littman:
An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning.
ICML 2008: 752-759 |
| 105 | EE | Ali Nouri,
Michael L. Littman:
Multi-resolution Exploration in Continuous Spaces.
NIPS 2008: 1209-1216 |
| 104 | EE | Michael L. Littman:
Autonomous Model Learning for Reinforcement Learning.
QEST 2008: 3 |
| 103 | EE | Enrique Munoz de Cote,
Michael L. Littman:
A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games.
UAI 2008: 419-426 |
| 102 | EE | Emma Brunskill,
Bethany R. Leffler,
Lihong Li,
Michael L. Littman,
Nicholas Roy:
CORL: A Continuous-state Offset-dynamics Reinforcement Learner.
UAI 2008: 53-61 |
| 101 | EE | David L. Roberts,
Charles L. Isbell,
Michael L. Littman:
Optimization problems involving collections of dependent objects.
Annals OR 163(1): 255-270 (2008) |
| 100 | EE | Alexander L. Strehl,
Michael L. Littman:
An analysis of model-based Interval Estimation for Markov Decision Processes.
J. Comput. Syst. Sci. 74(8): 1309-1331 (2008) |
| 2007 |
|---|
| 99 | | Bethany R. Leffler,
Michael L. Littman,
Timothy Edmunds:
Efficient Reinforcement Learning with Relocatable Action Models.
AAAI 2007: 572-577 |
| 98 | | Alexander L. Strehl,
Carlos Diuk,
Michael L. Littman:
Efficient Structure Learning in Factored-State MDPs.
AAAI 2007: 645-650 |
| 97 | EE | Thomas J. Walsh,
Ali Nouri,
Lihong Li,
Michael L. Littman:
Planning and Learning in Environments with Delayed Feedback.
ECML 2007: 442-453 |
| 96 | EE | Ronald Parr,
Christopher Painter-Wakefield,
Lihong Li,
Michael L. Littman:
Analyzing feature generation for value-function approximation.
ICML 2007: 737-744 |
| 95 | EE | Alexander L. Strehl,
Michael L. Littman:
Online Linear Regression and Its Application to Model-Based Reinforcement Learning.
NIPS 2007 |
| 94 | EE | Martin Zinkevich,
Amy R. Greenwald,
Michael L. Littman:
A hierarchy of prescriptive goals for multiagent learning.
Artif. Intell. 171(7): 440-447 (2007) |
| 93 | EE | Amy R. Greenwald,
Michael L. Littman:
Introduction to the special issue on learning and computational game theory.
Machine Learning 67(1-2): 3-6 (2007) |
| 2006 |
|---|
| 92 | | David L. Roberts,
Mark J. Nelson,
Charles Lee Isbell Jr.,
Michael Mateas,
Michael L. Littman:
Targeting Specific Distributions of Trajectories in MDPs.
AAAI 2006 |
| 91 | EE | Carlos Diuk,
Alexander L. Strehl,
Michael L. Littman:
A hierarchical approach to efficient reinforcement learning in deterministic domains.
AAMAS 2006: 313-319 |
| 90 | EE | Alexander L. Strehl,
Lihong Li,
Eric Wiewiora,
John Langford,
Michael L. Littman:
PAC model-free reinforcement learning.
ICML 2006: 881-888 |
| 89 | EE | Alexander L. Strehl,
Chris Mesterharm,
Michael L. Littman,
Haym Hirsh:
Experience-efficient learning in associative bandit problems.
ICML 2006: 889-896 |
| 88 | EE | Michael L. Littman,
Nishkam Ravi,
Arjun Talwar,
Martin Zinkevich:
An Efficient Optimal-Equilibrium Algorithm for Two-player Game Trees.
UAI 2006 |
| 87 | EE | Alexander L. Strehl,
Lihong Li,
Michael L. Littman:
Incremental Model-based Learners With Formal Learning-Time Guarantees.
UAI 2006 |
| 2005 |
|---|
| 86 | | Lihong Li,
Michael L. Littman:
Lazy Approximation for Solving Continuous Finite-Horizon MDPs.
AAAI 2005: 1175-1180 |
| 85 | | Nishkam Ravi,
Nikhil Dandekar,
Preetham Mysore,
Michael L. Littman:
Activity Recognition from Accelerometer Data.
AAAI 2005: 1541-1546 |
| 84 | EE | Alexander L. Strehl,
Michael L. Littman:
A theoretical analysis of Model-Based Interval Estimation.
ICML 2005: 856-863 |
| 83 | EE | Martin Zinkevich,
Amy R. Greenwald,
Michael L. Littman:
Cyclic Equilibria in Markov Games.
NIPS 2005 |
| 82 | EE | Bethany R. Leffler,
Michael L. Littman,
Alexander L. Strehl,
Thomas J. Walsh:
Efficient Exploration With Latent Structure.
Robotics: Science and Systems 2005: 81-88 |
| 81 | | Nicholas L. Cassimatis,
Sean Luke,
Simon D. Levy,
Ross 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 P. Singh,
Shlomo Argamon,
Shlomo Dubnov:
Reports on the 2004 AAAI Fall Symposia.
AI Magazine 26(1): 98-102 (2005) |
| 80 | EE | Peter D. Turney,
Michael L. Littman,
Jeffrey Bigham,
Victor Shnayder:
Combining Independent Modules in Lexical Multiple-Choice Problems
CoRR abs/cs/0501018: (2005) |
| 79 | EE | Peter D. Turney,
Michael L. Littman:
Corpus-based Learning of Analogies and Semantic Relations
CoRR abs/cs/0508103: (2005) |
| 78 | EE | Michael L. Littman,
Peter Stone:
A polynomial-time Nash equilibrium algorithm for repeated games.
Decision Support Systems 39(1): 55-66 (2005) |
| 77 | EE | Håkan L. S. Younes,
Michael L. Littman,
David Weissman,
John Asmuth:
The First Probabilistic Track of the International Planning Competition.
J. Artif. Intell. Res. (JAIR) 24: 851-887 (2005) |
| 76 | EE | Peter D. Turney,
Michael L. Littman:
Corpus-based Learning of Analogies and Semantic Relations.
Machine Learning 60(1-3): 251-278 (2005) |
| 2004 |
|---|
| 75 | | Michael L. Littman,
Nishkam Ravi,
Eitan Fenson,
Rich Howard:
An Instance-Based State Representation for Network Repair.
AAAI 2004: 287-292 |
| 74 | EE | Michael L. Littman,
Nishkam Ravi,
Eitan Fenson,
Rich Howard:
Reinforcement Learning for Autonomic Network Repair.
ICAC 2004: 284-285 |
| 73 | EE | Alexander L. Strehl,
Michael L. Littman:
An Empirical Evaluation of Interval Estimation for Markov Decision Processes.
ICTAI 2004: 128-135 |
| 2003 |
|---|
| 72 | EE | Michael L. Littman,
Peter Stone:
A polynomial-time nash equilibrium algorithm for repeated games.
ACM Conference on Electronic Commerce 2003: 48-54 |
| 71 | EE | Michael L. Littman:
Tutorial: Learning Topics in Game-Theoretic Decision Making.
COLT 2003: 1 |
| 70 | | Satinder P. Singh,
Michael L. Littman,
Nicholas K. Jong,
David Pardoe,
Peter Stone:
Learning Predictive State Representations.
ICML 2003: 712-719 |
| 69 | | Peter D. Turney,
Michael L. Littman,
Jeffrey Bigham,
Victor Shnayder:
Combining independent modules in lexical multiple-choice problems.
RANLP 2003: 101-110 |
| 68 | EE | Peter D. Turney,
Michael L. Littman:
Measuring praise and criticism: Inference of semantic orientation from association.
ACM Trans. Inf. Syst. 21(4): 315-346 (2003) |
| 67 | | Yukio Ohsawa,
Peter McBurney,
Simon Parsons,
Christopher A. Miller,
Alan C. Schultz,
Jean Scholtz,
Michael A. Goodrich,
Eugene Santos Jr.,
Benjamin Bell,
Charles Lee Isbell Jr.,
Michael L. Littman:
AAAI-2002 Fall Symposium Series.
AI Magazine 24(1): 95-98 (2003) |
| 66 | EE | Stephen M. Majercik,
Michael L. Littman:
Contingent planning under uncertainty via stochastic satisfiability.
Artif. Intell. 147(1-2): 119-162 (2003) |
| 65 | EE | Peter D. Turney,
Michael L. Littman:
Measuring Praise and Criticism: Inference of Semantic Orientation from Association
CoRR cs.CL/0309034: (2003) |
| 64 | EE | Peter D. Turney,
Michael L. Littman,
Jeffrey Bigham,
Victor Shnayder:
Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems
CoRR cs.CL/0309035: (2003) |
| 63 | EE | Peter D. Turney,
Michael L. Littman:
Learning Analogies and Semantic Relations
CoRR cs.LG/0307055: (2003) |
| 62 | EE | Peter Stone,
Robert E. Schapire,
Michael L. Littman,
János A. Csirik,
David A. McAllester:
Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions.
J. Artif. Intell. Res. (JAIR) 19: 209-242 (2003) |
| 2002 |
|---|
| 61 | EE | Paul S. A. Reitsma,
Peter Stone,
János Csirik,
Michael L. Littman:
Randomized strategic demand reduction: getting more by asking for less.
AAMAS 2002: 162-163 |
| 60 | EE | Peter Stone,
Robert E. Schapire,
János A. Csirik,
Michael L. Littman,
David A. McAllester:
ATTac-2001: A Learning, Autonomous Bidding Agent.
AMEC 2002: 143-160 |
| 59 | EE | Paul S. A. Reitsma,
Peter Stone,
János A. Csirik,
Michael L. Littman:
Self-Enforcing Strategic Demand Reduction.
AMEC 2002: 289-306 |
| 58 | | Robert E. Schapire,
Peter Stone,
David A. McAllester,
Michael L. Littman,
János A. Csirik:
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation.
ICML 2002: 546-553 |
| 57 | EE | Michail G. Lagoudakis,
Ronald Parr,
Michael L. Littman:
Least-Squares Methods in Reinforcement Learning for Control.
SETN 2002: 249-260 |
| 56 | EE | Michael L. Littman,
Greg A. Keim,
Noam M. Shazeer:
A probabilistic approach to solving crossword puzzles.
Artif. Intell. 134(1-2): 23-55 (2002) |
| 55 | EE | Peter D. Turney,
Michael L. Littman:
Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus
CoRR cs.LG/0212012: (2002) |
| 2001 |
|---|
| 54 | EE | Michael L. Littman,
Peter Stone:
Implicit Negotiation in Repeated Games.
ATAL 2001: 393-404 |
| 53 | EE | Peter Stone,
Michael L. Littman,
Satinder P. Singh,
Michael J. Kearns:
ATTac-2000: an adaptive autonomous bidding agent.
Agents 2001: 238-245 |
| 52 | | Michael L. Littman:
Friend-or-Foe Q-learning in General-Sum Games.
ICML 2001: 322-328 |
| 51 | EE | Michael L. Littman,
Richard S. Sutton,
Satinder P. Singh:
Predictive Representations of State.
NIPS 2001: 1555-1561 |
| 50 | EE | Sanjoy Dasgupta,
Michael L. Littman,
David A. McAllester:
PAC Generalization Bounds for Co-training.
NIPS 2001: 375-382 |
| 49 | EE | Michael L. Littman,
Michael J. Kearns,
Satinder P. Singh:
An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games.
NIPS 2001: 817-823 |
| 48 | EE | Michael J. Kearns,
Michael L. Littman,
Satinder P. Singh:
Graphical Models for Game Theory.
UAI 2001: 253-260 |
| 47 | EE | János A. Csirik,
Michael L. Littman,
Satinder P. Singh,
Peter Stone:
FAucS : An FCC Spectrum Auction Simulator for Autonomous Bidding Agents.
WELCOM 2001: 139-151 |
| 46 | EE | Michail G. Lagoudakis,
Michael L. Littman:
Learning to Select Branching Rules in the DPLL Procedure for Satisfiability.
Electronic Notes in Discrete Mathematics 9: 344-359 (2001) |
| 45 | EE | Peter Stone,
Michael L. Littman,
Satinder P. Singh,
Michael J. Kearns:
ATTac-2000: An Adaptive Autonomous Bidding Agent.
J. Artif. Intell. Res. (JAIR) 15: 189-206 (2001) |
| 44 | | Michael L. Littman,
Stephen M. Majercik,
Toniann Pitassi:
Stochastic Boolean Satisfiability.
J. Autom. Reasoning 27(3): 251-296 (2001) |
| 2000 |
|---|
| 43 | | Michail G. Lagoudakis,
Michael L. Littman:
Reinforcement Learning for Algorithm Selection.
AAAI/IAAI 2000: 1081 |
| 42 | | Jiefu Shi,
Michael L. Littman:
Towards Approximately Optimal Poker.
AAAI/IAAI 2000: 1094 |
| 41 | EE | Jiefu Shi,
Michael L. Littman:
Abstraction Methods for Game Theoretic Poker.
Computers and Games 2000: 333-345 |
| 40 | EE | Michael L. Littman:
Review: Computer Language Games.
Computers and Games 2000: 396-404 |
| 39 | | Fan Jiang,
Michael L. Littman:
Approximate Dimension Equalization in Vector-based Information Retrieval.
ICML 2000: 423-430 |
| 38 | | Michail G. Lagoudakis,
Michael L. Littman:
Algorithm Selection using Reinforcement Learning.
ICML 2000: 511-518 |
| 37 | | Justin A. Boyan,
Michael L. Littman:
Exact Solutions to Time-Dependent MDPs.
NIPS 2000: 1026-1032 |
| 36 | | Sebastian Thrun,
Michael L. Littman:
A Review of Reinforcement Learning.
AI Magazine 21(1): 103-105 (2000) |
| 35 | | Satinder P. Singh,
Tommi Jaakkola,
Michael L. Littman,
Csaba Szepesvári:
Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms.
Machine Learning 38(3): 287-308 (2000) |
| 1999 |
|---|
| 34 | | Noam M. Shazeer,
Michael L. Littman,
Greg A. Keim:
Solving Crossword Puzzles as Probabilistic Constraint Satisfaction.
AAAI/IAAI 1999: 156-162 |
| 33 | | Stephen M. Majercik,
Michael L. Littman:
Contingent Planning Under Uncertainty via Stochastic Satisfiability.
AAAI/IAAI 1999: 549-556 |
| 32 | | Michael L. Littman:
Initial Experiments in Stochastic Satisfiability.
AAAI/IAAI 1999: 667-672 |
| 31 | | Greg A. Keim,
Noam M. Shazeer,
Michael L. Littman,
Sushant Agarwal,
Catherine M. Cheves,
Joseph Fitzgerald,
Jason Grosland,
Fan Jiang,
Shannon Pollard,
Karl Weinmeister:
PROVERB: The Probabilistic Cruciverbalist.
AAAI/IAAI 1999: 710-717 |
| 30 | | Michael L. Littman,
Greg A. Keim,
Noam M. Shazeer:
Solving Crosswords with PROVERB.
AAAI/IAAI 1999: 914-915 |
| 29 | | Giuseppe De Giacomo,
Marie desJardins,
Dolores Cañamero,
Glenn S. Wasson,
Michael L. Littman,
Gerard Allwein,
Kim Marriott,
Bernd Meyer,
Barbara Webb,
Tom Con:
The AAAI Fall Symposia.
AI Magazine 20(3): 87-89 (1999) |
| 28 | | Csaba Szepesvári,
Michael L. Littman:
A Unified Analysis of Value-Function-Based Reinforcement Learning Algorithms.
Neural Computation 11(8): 2017-2060 (1999) |
| 1998 |
|---|
| 27 | | Stephen M. Majercik,
Michael L. Littman:
Using Caching to Solve Larger Probabilistic Planning Problems.
AAAI/IAAI 1998: 954-959 |
| 26 | | Stephen M. Majercik,
Michael L. Littman:
MAXPLAN: A New Approach to Probabilistic Planning.
AIPS 1998: 86-93 |
| 25 | | Michael L. Littman,
Fan Jiang,
Greg A. Keim:
Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus.
ICML 1998: 314-322 |
| 24 | EE | Leslie Pack Kaelbling,
Michael L. Littman,
Anthony R. Cassandra:
Planning and Acting in Partially Observable Stochastic Domains.
Artif. Intell. 101(1-2): 99-134 (1998) |
| 23 | EE | Michael L. Littman,
Judy Goldsmith,
Martin Mundhenk:
The Computational Complexity of Probabilistic Planning
CoRR cs.AI/9808101: (1998) |
| 22 | EE | Michael L. Littman,
Judy Goldsmith,
Martin Mundhenk:
The Computational Complexity of Probabilistic Planning.
J. Artif. Intell. Res. (JAIR) 9: 1-36 (1998) |
| 1997 |
|---|
| 21 | | Michael L. Littman:
Probabilistic Propositional Planning: Representations and Complexity.
AAAI/IAAI 1997: 748-754 |
| 20 | | Michael S. Fulkerson,
Michael L. Littman,
Greg A. Keim:
Speeding Safely: Multi-Criteria Optimization in Probabilistic Planning.
AAAI/IAAI 1997: 831 |
| 19 | EE | Bob Rehder,
Michael L. Littman,
Susan T. Dumais,
Thomas K. Landauer:
Automatic 3-Language Cross-Language Information Retrieval with Latent Semantic Indexing.
TREC 1997: 233-239 |
| 18 | EE | Judy Goldsmith,
Michael L. Littman,
Martin Mundhenk:
The Complexity of Plan Existence and Evaluation in Probabilistic Domains.
UAI 1997: 182-189 |
| 17 | EE | Anthony R. Cassandra,
Michael L. Littman,
Nevin Lianwen Zhang:
Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes.
UAI 1997: 54-61 |
| 1996 |
|---|
| 16 | | Michael L. Littman,
Csaba Szepesvári:
A Generalized Reinforcement-Learning Model: Convergence and Applications.
ICML 1996: 310-318 |
| 15 | EE | Eugene Charniak,
Glenn Carroll,
John Adcock,
Anthony R. Cassandra,
Yoshihiko Gotoh,
Jeremy Katz,
Michael L. Littman,
John McCann:
Taggers for Parsers.
Artif. Intell. 85(1-2): 45-57 (1996) |
| 14 | EE | Leslie Pack Kaelbling,
Michael L. Littman,
Andrew W. Moore:
Reinforcement Learning: A Survey
CoRR cs.AI/9605103: (1996) |
| 13 | | Leslie Pack Kaelbling,
Michael L. Littman,
Andrew P. Moore:
Reinforcement Learning: A Survey.
J. Artif. Intell. Res. (JAIR) 4: 237-285 (1996) |
| 1995 |
|---|
| 12 | | Michael L. Littman,
Anthony R. Cassandra,
Leslie Pack Kaelbling:
Learning Policies for Partially Observable Environments: Scaling Up.
ICML 1995: 362-370 |
| 11 | | Leslie Pack Kaelbling,
Michael L. Littman,
Anthony R. Cassandra:
Partially Observable Markov Decision Processes for Artificial Intelligence.
KI 1995: 1-17 |
| 10 | | Leslie Pack Kaelbling,
Michael L. Littman,
Anthony R. Cassandra:
Partially Observable Markov Decision Processes for Artificial Intelligence.
Reasoning with Uncertainty in Robotics 1995: 146-163 |
| 9 | EE | Michael L. Littman,
Thomas Dean,
Leslie Pack Kaelbling:
On the Complexity of Solving Markov Decision Problems.
UAI 1995: 394-402 |
| 1994 |
|---|
| 8 | | Anthony R. Cassandra,
Leslie Pack Kaelbling,
Michael L. Littman:
Acting Optimally in Partially Observable Stochastic Domains.
AAAI 1994: 1023-1028 |
| 7 | | Michael L. Littman:
Markov Games as a Framework for Multi-Agent Reinforcement Learning.
ICML 1994: 157-163 |
| 1993 |
|---|
| 6 | EE | Robert B. Allen,
Pascal Obry,
Michael L. Littman:
An interface for navigating clustered document sets returned by queries.
COOCS 1993: 166-171 |
| 5 | EE | Justin A. Boyan,
Michael L. Littman:
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach.
NIPS 1993: 671-678 |
| 1992 |
|---|
| 4 | EE | Laurence Brothers,
James D. Hollan,
Jakob Neilsen,
Scott Stornetta,
Steven P. Abney,
George W. Furnas,
Michael L. Littman:
Supporting Informal Communication via Ephemeral Interest Groups.
CSCW 1992: 84-90 |
| 1991 |
|---|
| 3 | EE | Dennis E. Egan,
Michael Lesk,
R. Daniel Ketchum,
Carol C. Lochbaum,
Joel R. Remde,
Michael L. Littman,
Thomas K. Landauer:
Hypertext for the Electronic Library? CORE Sample Results.
Hypertext 1991: 299-312 |
| 2 | | Michael L. Littman,
David H. Ackley:
Adaptation in Constant Utility Non-Stationary Environments.
ICGA 1991: 136-142 |
| 1989 |
|---|
| 1 | EE | David H. Ackley,
Michael L. Littman:
Generalization and Scaling in Reinforcement Learning.
NIPS 1989: 550-557 |
