2009
-
Carlos Diuk, Lihong Li and Bethany R. Leffler,
The Adaptive k-Meteorologists Problem and Its Application to Structure Learning and Feature Selection in Reinforcement Learning
, Proceedings of the Twenty-Sixth International Conference on Machine Learning (ICML-09)
, 2009.
-
Alexander L. Strehl and Lihong Li and Michael L. Littman,
Reinforcement learning in general MDPs: PAC analysis
, Journal of Machine Learning Research
, Pages 2413--2444
, 2009.
-
Emma Brunskill and Bethany R. Leffler and Lihong Li and Michael L. Littman and Nichlos Roy,
Provably efficient learning with typed parametric models
, Journal of Machine Learning Research
, Pages 1955--1988,
, 2009.
-
Thomas J. Walsh and Ali Nouri and Lihong Li and Michael L. Littman,
Planning and Learning in Environments with Delayed Feedback
, Journal of Autonomous Agents and Multi-Agent Systems
, Pages 83--105
, 2009.
-
Lihong Li and Michael L. Littman and Christopher R. Mansley,
Online Exploration in Least-Squares Policy Iteration
, Proceedings of the Eighth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-09)
, 2009.
-
Ali Nouri and Michael L. Littman,
Multi-resolution Exploration in Continuous Spaces
, Proceedings of Neural Information Processing Systems
, 2009.
-
Thomas J. Walsh and István Szita and Carlos Diuk and Michael L. Littman,
Exploring Compact Reinforcement-Learning Representations with Linear Regression
, Proceedings of The 25th Conference on Uncertainty in Artificial Intelligence (UAI-09)
, 2009.
-
John Asmuth and Lihong Li and Michael L. Littman and Ali Nouri and David Wingate,
A Bayesian Sampling Approach to Exploration in Reinforcement Learning
, Proceedings of The 25th Conference on Uncertainty in Artificial Intelligence (UAI-09)
, 2009.
2008
-
Monica Babes and Enrique Munoz de Cote and Michael Littman,
Social Reward Shaping in the Prisoner's Dilemma
, Autonomous Agents and Multiagent Systems 2008
, 2008.
-
John Asmuth and Michael L. Littman and Robert Zinkov,
Potential-based Shaping in Model-based Reinforcement Learning
, Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08)
, 2008.
-
Alexander L. Strehl and Michael L. Littman,
Online Linear Regression and Its Application to Model-Based Reinforcement Learning
, Proceedings of Neural Information Processing Systems
, 2008.
-
Lihong Li and Michael L. Littman and Thomas J. Walsh,
Knows What It Knows: A Framework for Self-Aware Learning
, Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML-08)
, 2008.
-
Lihong Li and Michael Littman,
Efficient Value-Function Approximation via Online Linear Regression
, International Symposium on Artificial Intelligence and Mathematics
, 2008.
-
Bethany R. Leffler and Christopher R. Mansley and Michael L. Littman,
Efficient Learning of Dynamics Models using Terrain Classification
, Proceedings of the International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems
, 2008.
-
Fusun Yaman and Thomas J. Walsh and Michael L. Littman and Marie desJardins,
Democratic Approximation of Lexicographic Preference Models
, Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML-08)
, 2008.
-
Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, and Nicholas Roy,
CORL: A Continuous-State Offset-Dynamics Reinforcement Learner
, Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI-08)
, 2008.
-
Carlos Diuk and Andre Cohen and Michael Littman,
An Object-Oriented Representation for Efficient Reinforcement Learning
, Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML-08)
, 2008.
-
Ronald Parr and Lihong Li and Gavin Taylor and Christopher Painter-Wakefield and Michael L. Littman,
An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning
, Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML-08)
, 2008.
-
Lihong Li,
A Worst-Case Comparison between Temporal Difference and Residual Gradient with Linear Function Approximation
, Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML-08)
, 2008.
-
Thomas J. Walsh and Michael L. Littman,
Efficient Learning of Action Schemas and Web-Service Descriptions
, Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08)
, 2008.
2007
-
Thomas J. Walsh and Michael L. Littman,
Planning with Conceptual Models Mined from User Behavior
, Proceedings of the AAAI-07 Workshop on Acquiring Planning Knowledge via Demonstration
, 2007.
-
Thomas J. Walsh and Ali Nouri and Lihong Li and Michael L. Littman,
Planning and Learning in Environments with Delayed Feedback
, Proceedings of the 18th European Conference on Machine Learning (ECML-07)
, 2007.
-
Fancong Zeng and Michael L. Littman,
Just-in-time Failure Detection
, ICAC-2007 Workshop on Adaptive Methods in Autonomic Computing Systems (AMACS)
, 2007.
-
Amy Greenwald and Michael L. Littman,
Introduction to the special issue on learning and computational game theory
, Machine Learning
, 2007.
-
Alexander L. Strehl and Carlos Diuk and Michael L. Littman,
Efficient Structure Learning in Factored-state MDPs
, Proceedings of the Twenty-Second National Conference on Artificial Intelligence (AAAI-07)
, 2007.
-
Bethany R. Leffler and Michael L. Littman and Timothy Edmunds,
Efficient Reinforcement Learning with Relocatable Action Models
, Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI-07)
, 2007.
-
Ronald Parr and Christopher Painter-Wakefield and Lihong Li and Michael Littman,
Analyzing Feature Generation for Value-Function Approximation
, International Conference on Machine Learning (ICML-2007)
, 2007.
-
Martin Zinkevich and Amy Greenwald and Michael L. Littman,
A hierarchy of prescriptive goals for multiagent learning
, Artificial Intelligence
, Pages 440--447
, 2007.
-
Thomas J. Walsh and Michael L. Littman,
A Multiple Representation Approach To Learning Dynamical Systems
, Computational Approaches to Representation Change During Learning and Development: AAAI Fall Symposium
, 2007.
2006
-
Lihong Li and Thomas J. Walsh and Michael L. Littman,
Towards a Unified Theory of State Abstraction for MDPs
, Ninth International Symposium on Artificial Intelligence and Mathematics
, 2006.
-
David L. Roberts and Mark J. Nelson and Isbell, Jr., Charles Lee and Michael Mateas and Michael L. Littman,
Targeting Specific Distributions of Trajectories in MDPs
, Proceedings of The Twenty-First National Conference on Artificial Intelligence
, 2006.
-
Alexander L. Strehl and Lihong Li and Michael L. Littman,
PAC Reinforcement Learning Bounds for RTDP and Rand-RTDP
, AAAI 2006 Workshop on Learning For Search
, 2006.
-
Alexander L. Strehl and Lihong Li and Eric Wiewiora and John Langford and Michael L. Littman,
PAC Model-free Reinforcement Learning
, Proceedings of the Twenty-third International Conference on Machine Learning (ICML-06)
, 2006.
-
Alexander L. Strehl and Lihong Li and Michael L. Littman,
Incremental Model-based Learners With Formal Learning-Time Guarantees
, Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI 2006)
, 2006.
-
Alexander L. Strehl and Chris Mesterharm and Michael L. Littman and Haym Hirsh,
Experience-Efficient Learning in Associative Bandit Problems
, Proceedings of the Twenty-third International Conference on Machine Learning (ICML-06)
, 2006.
-
Michael L. Littman and Nishkam Ravi and Arjun Talwar and Martin Zinkevich,
An Efficient Optimal-Equilibrium Algorithm for Two-Player Game Trees
, Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI-06)
, 2006.
-
Carlos Diuk and Michael Littman and Alexander Strehl,
A Hierarchical Approach to Efficient Reinforcement Learning in Deterministic Domains
, Fifth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-06)
, 2006.
2005
-
Hakan L. S. Younes and Michael L. Littman and David Weissman and John Asmuth,
The First Probabilistic Track of the International Planning Competition
, Journal of Artificial Intelligence Research 24, Pages 851-887
, 2005.
-
Lihong Li and Michael L. Littman,
Lazy Approximation for Solving Continuous Finite-Horizon MDPs
, The Twentieth National Conference on Artificial Intelligence
, Pages 1175--1180
, 2005.
-
Bethany R. Leffler and Michael L. Littman and Alexander L. Strehl and Thomas J. Walsh,
Efficient Exploration With Latent Structure
, Proceedings of Robotics: Science and Systems
, 2005.
-
Martin Zinkevich and Amy R. Greenwald and Michael L. Littman,
Cyclic Equilibria in Markov Games
, Advances in Neural Information Processing Systems 18
, 2005.
-
Nishkam Ravi and Nikhil Dandekar and Preetham Mysore and Michael L. Littman,
Activity Recognition from Accelerometer Data
, Seventeenth Innovative Applications of Artificial Intelligence Conference
, Pages 1541--1546
, 2005.
-
Alexander L. Strehl and Michael L. Littman,
A Theoretical Analysis of Model-Based Interval Estimation
, Proceedings of the Twenty-second International Conference on Machine Learning (ICML-05)
, Pages 857--864
, 2005.
2004
-
David LeRoux and Michael Littman,
Reinforcement Learning using LCS in Continuous State Space
, 2004.
-
Michael L. Littman and Nishkam Ravi and Eitan Fenson and Rich Howard,
Reinforcement Learning for Autonomic Network Repair
, 1st International Conference on Autonomic Computing (ICAC 2004)
, Pages 284--285
, 2004.
-
Alexander L. Strehl and Michael L. Littman,
An Empirical Evaluation of Interval Estimation for Markov Decision Processes
, The 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2004)
, Pages 128--135
, 2004.
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