- CS--1997--16 CrossLanguage Text Retrieval
- Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems
- Reinforcement Learning for Autonomic Network Repair Michael L. Littman and Nishkam Ravi
- Packet Routing in Dynamically Changing Networks
- CS--1999--5 Efficient Singular Value Decomposition
- An Interface for Navigating Clustered Document Sets Returned by Queries
- Equilibria? distribution
- Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes
- the case for Learning Correlated Equilibrium Policies
- Real-Life Multiagent Reinforcement Learning
- CS--1998--11 A Comparison of Two CorpusBased
- The Complexity of Plan Existence and Evaluation in Probabilistic Domains
- Learning a LanguageIndependent Representation for Terms From a Partially Aligned Corpus
- Reinforcement Learning using LCS in Continuous State Space IWLCS-2004 Extended Abstract
- MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
- Efficient dynamicprogramming updates in partially observable Markov decision processes
- Contingent Planning Under Uncertainty via Stochastic Satisfiability
- An Empirical Evaluation of Interval Estimation for Markov Decision Processes
- Gilks W R, Richardson S, Spiegelhalter D J 1996 Marko Chain Monte Carlo in Practice. Chapman and Hall, London
- This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research
- a plannermodel policyPlanning
- Automatic 3-Language Cross-Language Information Retrieval with Latent Semantic Indexing
- EFFICIENT MODEL-BASED EXPLORATION IN CONTINUOUS STATE-SPACE ENVIRONMENTS
- AN OBJECT-ORIENTED REPRESENTATION FOR EFFICIENT REINFORCEMENT LEARNING
- A UNIFYING FRAMEWORK FOR COMPUTATIONAL REINFORCEMENT LEARNING
- LEARNING-BASED ROUTE MANAGEMENT IN WIRELESS AD HOC NETWORKS
- PROBABLY APPROXIMATELY CORRECT (PAC) EXPLORATION IN REINFORCEMENT LEARNING
- Knows What It Knows: A Framework For Self-Aware Learning Lihong Li lihong@cs.rutgers.edu
- Reinforcement Learning Benchmarks and Bake-offs II
- Introduction to Real-Life Reinforcement Learning
- On the Risks and Rewards of Coordination in Multiagent
- Learning, Equilibria, Limitations, and Robots*
- Independent Components in Text Thomas Kolenda and Lars Kai Hansen
- Introduction to the Probabilistic Planning Track Michael L. Littman
- PPDDL1.0: The Language for the Probabilistic Part of IPC-4 Hakan L. S. Younes
- Symbolic Heuristic Search for Probabilistic Planning Zhengzhu Feng
- NMRDPP: Decision-Theoretic Planning with Control Knowledge.
- FCPlanner: A Planning Strategy for First-Order MDPs Eldar Karabaev
- Probapop: Probabilistic Partial-Order Planning Nilufer Onder Garrett C. Whelan Li Li
- Probabilistic Reachability Analysis for Structured Markov Decision Processes Florent Teichteil-Konigsbuch and Patrick Fabiani
- Plastic Individuals in Evolving Populations page 1 Preface to Chapter by S. Shafir and J. Roughgarden
- Algorithms for Sequential Decision Making Michael Lederman Littman
- Algorithmica 60 (2011) 180-195 Submitted 8/11; Published 4/11 Algorithmic Foundations of Quantum Data Analytics
- Probabilistic Propositional Planning: Representations and Complexity Michael L. Littman
- An optimizationbased categorization of reinforcement learning environments
- Using Caching to Solve Larger Probabilistic Planning Problems Stephen M. Majercik and Michael L. Littman
- Cost-Sensitive Fault Remediation for Autonomic Computing Michael L. Littman and Thu Nguyen and Haym Hirsh
- Automatic CrossLanguage Information Retrieval using Latent Semantic Indexing
- ThreeDimensional Tutte Embedding Kiran Chilakamarri
- JUST-IN-TIME AND JUST-IN-PLACE DEADLOCK RESOLUTION
- Adaptation in constant utility nonstationary environments
- Approximate Dimension Equalization in Vector-based Information Retrieval
- Probabilistic Propositional Planning: Representations and Complexity Michael L. Littman
- MultiAgent Learning: Theory and Practice (Day 1) Friday December 13, 2002
- Unsupervised Document Classification using Sequential Information Maximization
- An Instance-based State Representation for Network Repair Michael L. Littman and Nishkam Ravi
- The Complexity of Plan Existence and Evaluation in Probabilistic Domains
- Solving Crossword Puzzles as Probabilistic Constraint Satisfaction Noam M. Shazeer, Michael L. Littman, Greg A. Keim
- Automatic CrossLanguage Retrieval Using Latent Semantic Indexing We describe a method for fully automated crosslanguage
- Exact Solutions to TimeDependent MDPs Justin A. Boyan
- Journal of Artificial Intelligence Research 9 (1998) 1--36 Submitted 1/98; published 8/98 The Computational Complexity of Probabilistic Planning
- Simulations combining evolution and learning Michael Littman
- CS--1996--18 Probabilistic Propositional Planning
- Proverb: The Probabilistic Cruciverbalist Greg A. Keim, Noam M. Shazeer, Michael L. Littman,
- A statistical method for languageindependent representation of the topical content of text segments
- Convergence Results for SingleStep OnPolicy ReinforcementLearning Algorithms
- Journal of Artificial Intelligence Research 6 (1997) 999--1022 Submitted 7/97; published 7/97 The Computational Complexity of
- Review: Computer Language Games Michael L. Littman
- An Ecient, Exact Algorithm for Solving Tree-Structured Graphical Games
- A Unified Analysis of ValueFunctionBased ReinforcementLearning Algorithms
- Reinforcement Learning: A Survey Leslie Pack Kaelbling \Lambda (lpk@cs.brown.edu)
- The Witness Algorithm: Solving Partially Observable Markov Decision Processes
- Algorithms for Sequential Decision Making Michael Lederman Littman
- A Polynomial-time Nash Equilibrium Algorithm for Repeated Games
- MaxiBook: UserCentered Hypertext on an ASCII Michael L. Littman
- Friend-or-Foe Q-learning in General-Sum Games Michael L. Littman mlittman@research.att.com
- Copyright c 2000 by Fan Jiang All rights reserved
- Algorithms for Informed Cows Ming-Yang Kao and Michael L. Littman
- Algorithms for Sequential Decision Making Michael Lederman Littman
- Representations and Algorithms for Exact Time-Dependent MDPs
- Journal of Cognitive Systems Research 1 (2001) 000000 4 www.elsevier.com/locate/cogsys
- Graphical Models for Game Theory Michael Kearns #
- Automatic 3Language CrossLanguage Information Retrieval with Latent Semantic Indexing
- Combining Exploration and Control in Reinforcement Learning: The Convergence of SARSA
- Reinforcement Learning for Selfish Load Balancing in a
- A Distributed Reinforcement Learning Scheme
- Reinforcement unpredictable
- The attainment of equilibrium re-quires a disequilibrium process.
- Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms
- International Planning Competition 2004 (IPC-4)
- A Comparison of Two Corpus-Based Methods for
- Initial Experiments in Stochastic Satisfiability Michael L. Littman
- Generalization and scaling in reinforcement David H. Ackley
- Using Caching to Solve Larger Probabilistic Planning Problems Stephen M. Majercik and Michael L. Littman
- Solving Crosswords with Proverb Michael L. Littman, Greg A. Keim, Noam M. Shazeer
- Machine Learning: Proceedings of the Nineteenth International Conference, 2002. Modeling Auction Price Uncertainty
- Packet Routing in Dynamically Changing Networks
- Implicit Negotiation in Repeated Games Michael L. Littman and Peter Stone
- A Unified Analysis of ValueFunctionBased ReinforcementLearning Algorithms
- Speeding Safely: Multicriteria Optimization in Probabilistic Planning Michael S. Fulkerson, Michael L. Littman, and Greg A. Keim
- A Distributed Reinforcement Learning Scheme for Network Routing
- MultiAgent Learning: Theory and Practice (Day 2) Saturday December 14, 2002
- Exploration via Model-based Interval Estimation Alexander L. Strehl strehl@cs.rutgers.edu
- Acting Optimally in Partially Observable Stochastic Domains Anthony R. Cassandra
- Artificial Intelligence 134 (2002) 2355 A probabilistic approach
- EFFICIENT LEARNING OF RELATIONAL MODELS FOR SEQUENTIAL DECISION MAKING
- MAXPLAN: A New Approach to Probabilistic Planning Stephen M. Majercik and Michael L. Littman
- Algorithms for Informed Cows MingYang Kao and Michael L. Littman
- A Comparison of Two CorpusBased Methods for Translingual Information Retrieval
- Perception-Based Generalization in Model-Based Reinforcement Learning
- y 1= y 1=y 0= y 0= y4
- Learning Reactive Policies for Probabilistic Planning Domains SungWook Yoon and Alan Fern and Robert Givan
- A polynomialtime Nash equilibrium algorithm for repeated games #
- A Generalized ReinforcementLearning Model: Convergence and Applications
- Packet Routing and Reinforcement Learning: Estimating Shortest Paths in Dynamic Graphs
- LargeScale Planning Under Uncertainty: A Survey Michael L. Littman and Stephen M. Majercik
- Journal of Artificial Intelligence Research 15 (2001) 189206 Submitted 2/01; published 9/01 ATTac2000: An Adaptive Autonomous Bidding Agent
- University, mlittman@cs.duke.edu DARPA
- Visualizing the embedding of objects in Euclidean space Michael Littman, Deborah F. Swayne, Nathaniel Dean, and Andreas Buja
- Selfenforcing Strategic Demand Reduction Paul S. A. Reitsma 1 , Peter Stone 2 , J anos A. Csirik 3 , and Michael L. Littman 4
- How Combine Expert (or Novice) Advice when Actions Impact the Environment
- Abstraction Methods for Game Theoretic Poker Jiefu Shi 1 and Michael L. Littman 2
- Hidden State and ShortTerm Memory: A bibliography
- Memoryless policies: theoretical limitations and practical results Michael L. Littman
- IPC 2004 Probabilistic Planning Track: FAQ 0.1 Michael L. Littman
