Refereed Publications

Fan Jiang and Michael L. Littman. Approximate dimension equalization in vector-based information retrieval. In Proceedings of the Seventeenth International Conference on Machine Learning, to appear, 2000. (postscript)

Michail G. Lagoudakis and Michael L. Littman. Algorithm selection using reinforcement learning. In Proceedings of the Seventeenth International Conference on Machine Learning, to appear, 2000.*

Michael L. Littman, Stephen M. Majercik, and Toniann Pitassi. Stochastic Boolean satisfiability. Journal of Automated Reasoning, 2000. To appear. (postscript) *

Csaba Szepesvári and Michael L. Littman. A unified analysis of value-function-based reinforcement-learning algorithms. Neural Computation, 11:8, pages 2017-2059, 1999. (near final version in postscript)*

Greg A. Keim, Noam Shazeer, Michael L. Littman, Sushant Agarwal, Catherine M. Cheves, Joseph Fitzgerald, Jason Grosland, Fan Jiang, Shannon Pollard, and Karl Weinmeister. Proverb: The probabilistic cruciverbalist. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages 710-717, 1999. (abstract, postscript) Winner of the "Best Paper" Award.

Michael L. Littman, Greg A. Keim, and Noam M. Shazeer. Solving Crosswords with Proverb. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages 914-915, 1999. (postscript)

Noam M. Shazeer, Michael L. Littman, and Greg A. Keim. Solving crossword puzzles as probabilistic constraint satisfaction. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages 156-162, 1999. Earlier version: Technical Report CS-99-03, Duke University, Department of Computer Science, Durham, NC, February 1999. (abstract, postscript (draft))

Michael L. Littman. Initial Experiments in stochastic satisfiability. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages 667-672, 1999. (abstract, postscript)*

Stephen M. Majercik and Michael L. Littman. Contingent planning under uncertainty via probabilistic satisfiability. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages, 549-556, 1999. (abstract, postscript)*

Leslie Pack Kaelbling, Michael L. Littman and Anthony R. Cassandra. Planning and Acting in Partially Observable Stochastic Domains. Artificial Intelligence, 101: 1-2, pages 99-134, 1998.(official pdf, early version in compressed postscript, pdf). Also available as Brown University Technical Report CS-96-08. (abstract, revised draft in postscript)*

Satinder Singh, Tommi Jaakkola, Michael L. Littman and Csaba Szepesvári. Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms. Machine Learning, 2000. Volume 39, pages 287--308. (draft in postscript)*

Stephen M. Majercik and Michael L. Littman. Using caching to solve larger probabilistic planning problems. In AAAI, pages 954-959, 1998. (postscript, abstract).*

Stephen M. Majercik and Michael L. Littman. MAXPLAN: A new approach to probabilistic planning. In AIPS, pages 86--93, 1998. (postscript, abstract).*

Michael L. Littman, Judy Goldsmith, and Martin Mundhenk. The computational complexity of probabilistic planning. Journal of Artificial Intelligence Research, volume 9, pages 1--36, 1998. (postscript, official JAIR version, abstract)*

Michael L. Littman, Fan Jiang, and Greg A. Keim. Learning a language-independent representation for terms from a partially aligned corpus. Proceedings of the Fifteenth International Conference on Machine Learning, pages 314-322, 1998. (postscript)

Michael L. Littman and Stephen M. Majercik. Large-Scale Planning Under Uncertainty: A Survey. In Workshop on Planning and Scheduling for Space, pages 27:1--8, 1997. (postscript)*

Anthony Cassandra, Michael L. Littman, and Nevin L. Zhang. Incremental pruning: A simple, fast, exact algorithm for partially observable Markov decision processes. In Dan Geiger and Prakash Pundalik Shenoy, editors, Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI--97), pages 54--61, San Francisco, CA, 1997. Morgan Kaufmann. (postscript, abstract)*

Judy Goldsmith, Michael L. Littman, and Martin Mundhenk. The complexity of plan existence and evaluation in probabilistic domains. In Dan Geiger and Prakash Pundalik Shenoy, editors, Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI--97), pages 182--189, San Francisco, CA, 1997. Morgan Kaufmann. (abstract, postscript, Duke CS Technical Report CS-1997-07)*

Ming-Yang Kao and Michael L. Littman. Algorithms for informed cows. AAAI-97 Workshop on On-Line Search, 1997 (postscript)*

Michael L. Littman. Probabilistic propositional planning: Representations and complexity. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, pages 748--754, 1997. (postscript).*

Michael S. Fulkerson, Michael L. Littman, and Greg A. Keim. Speeding Safely: Multi-criteria optimization in probabilistic planning. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, page 831, 1997 (postscript).*

Eugene Charniak, Glenn Carroll, John Adcock, Anthony Cassandra, Yoshihiko Gotoh, Jeremy Katz, Michael Littman, and John McCann. Taggers for parsers. Artificial Intelligence, 85 (1-2): 45--57, 1996. (postscript, techreport page)

Michael L. Littman and Csaba Szepesvári. A generalized reinforcement-learning model: Convergence and applications. In Proceedings of the Thirteenth International Conference on Machine Learning, pages 310-318, 1996. (abstract, postscript)

Leslie Pack Kaelbling, Michael L. Littman, and Andrew W. Moore. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4:237-285, 1996. (draft in postscript, official JAIR version)

Michael Littman. Simulations combining evolution and learning. In Rik K. Belew and Melanie Mitchell, editors, Adaptive Individuals in Evolving Populations: Models and Algorithms: Santa Fe Institute Studies in the Sciences of Complexity, volume XXVI, pages 465--477. Addison-Wesley Publishing Company, Reading, MA, 1996. (draft in postscript, book information)

Leslie Pack Kaelbling, Michael L. Littman, and Andrew W. Moore. An introduction to reinforcement learning. In Luc Steels, editor, Proceedings of the NATO advanced study institute on the biology and technology of intelligent autonomous agents, volume 144, Berlin, 1995. Springer-Verlag.

Kiran Chilakamarri, Nathaniel Dean, and Michael Littman. Three-dimensional Tutte embedding. Congressus Numerantium, 107:129-140, 1995. (figureless version in postscript)

Michael Littman, Anthony Cassandra, and Leslie Kaelbling. Learning policies for partially observable environments: Scaling up. In Armand Prieditis and Stuart Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning, pages 362--370, San Francisco, CA, 1995. Morgan Kaufmann. (postscript, Brown extended tech report, abstract)

Michael L. Littman, Thomas L. Dean, and Leslie Pack Kaelbling. On the complexity of solving Markov decision problems. In Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence (UAI--95), Montreal, Quebec, Canada, 1995. (postscript, abstract)

David H. Ackley and Michael L. Littman. Altruism in the evolution of communication. In Rodney A. Brooks and Pattie Maes, editors, Artificial Life IV: Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems, pages 40--49, Cambridge, MA, 1994. Bradford Books/MIT Press.

Michael L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Proceedings of the Eleventh International Conference on Machine Learning, pages 157--163, San Francisco, CA, 1994. Morgan Kaufmann. (abstract, postscript, pdf)

Michael L. Littman. Memoryless policies: Theoretical limitations and practical results. In Dave Cliff, Philip Husbands, Jean-Arcady Meyer, and Stewart W. Wilson, editors, From Animals to Animats 3: Proceedings of the Third International Conference on Simulation of Adaptive Behavior, Cambridge, MA, 1994. MIT Press. (postscript)

Anthony R. Cassandra, Leslie Pack Kaelbling, and Michael L. Littman. Acting optimally in partially observable stochastic domains. In Proceedings of the Twelfth National Conference on Artificial Intelligence, Seattle, WA, 1994. (tech report page at Brown, postscript (figures broken))

Justin A. Boyan and Michael L. Littman. Packet routing in dynamically changing networks: A reinforcement learning approach. In Jack D. Cowan, Gerald Tesauro, and Joshua Alspector, editors, Advances in Neural Information Processing Systems, volume 6, pages 671--678. Morgan Kaufmann, San Francisco CA, 1993. (abstract, postscript, pdf)

Robert Allen, Pascal Obry, and Michael Littman. An interface for navigating clustered document sets returned by queries. In Conference on Organizational Computing Systems (COOCS), pages 166--171. SIGOIS, Milpitas, November 1993. (postscript)

Michael L. Littman and Justin A. Boyan. A distributed reinforcement learning scheme for network routing. In Joshua Alspector, Rodney Goodman, and Timothy X. Brown, editors, Proceedings of the 1993 International Workshop on Applications of Neural Networks to Telecommunications, pages 45--51. Lawrence Erlbaum Associates, Hillsdale NJ, 1993. (abstract, postscript)

David H. Ackley and Michael L. Littman. A case for distributed Lamarckian evolution. In C. Langton, C. Taylor, J. D. Farmer, and S. Ramussen, editors, Artificial Life III: Santa Fe Institute Studies in the Sciences of Complexity, volume 10, pages 487--509. Addison-Wesley, Redwood City, CA, 1993.

Michael L. Littman, Deborah F. Swayne, Nathaniel Dean, and Andreas Buja. Visualizing the embedding of objects in euclidean space. In H. Joseph Newton, editor, Computing Science and Statistics, Proceedings of the 24th symposium on the Interface, volume 24, pages 208--217. Interface Foundation of North America, 1992. (abstract, postscript)

Laurence Brothers, James Hollan, W. Scott Stornetta, Jakob Nielsen, Steven Abney, George W. Furnas, and Michael L. Littman. Supporting informal communication via ephemeral interest groups. In Proceedings of the Computer Supported Cooperative Work (CSCW) '92 conference. The Association For Computing Machinery, Toronto, November 1992.

Michael L. Littman. An optimization-based categorization of reinforcement learning environments. In I. H. Meyer, H. Roithlat, and S. Wilson, editors, From Animals to Animats: Proceedings of the Second International Conference on Simulation and Adaptive Behavior. MIT Press, 1992. (postscript)

David H. Ackley and Michael L. Littman. Interactions between learning and evolution. In C. Langton, C. Taylor, J. D. Farmer, and S. Ramussen, editors, Artificial Life II: Santa Fe Institute Studies in the Sciences of Complexity, volume 10, pages 487--509. Addison-Wesley, Redwood City, CA, 1991.

Dennis E. Egan, Michael E. Lesk, R. Daniel Ketchum, Carol C. Lochbaum, Joel R. Remde, Michael L. Littman, and Thomas K. Landauer. Hypertext for the electronic library? CORE sample results. In Proceedings of Hypertext '91. Association of Computing Machinery, 1991.

Michael L. Littman and David H. Ackley. Adaptation in constant utility non-stationary environments. In Rik K. Belew and Lashon Booker, editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 136--142, San Mateo, CA, 1991. Morgan Kaufmann. (figureless version in postscript)

Thomas K. Landauer and Michael L. Littman. A statistical method for language-independent representation of the topical content of text segments. In Proceedings of the Eleventh International Conference: Expert Systems and Their Applications, volume 8, pages 77--85. Avignon France, May 1991.

Richard J. Gerrig and Michael L. Littman. Disambiguation by community membership. Memory and Cognition, 18(4):331--338, 1990.

Thomas K. Landauer and Michael L. Littman. Fully automatic cross-language document retrieval using latent semantic indexing. In Proceedings of the Sixth Annual Conference of the UW Centre for the New Oxford English Dictionary and Text Research, pages 31--38. UW Centre for the New OED and Text Research, Waterloo Ontario, October 1990. (abstract, version in postscript)

David H. Ackley and Michael L. Littman. Generalization and scaling in reinforcement learning. In D. S. Touretzky, editor, Advances in Neural Information Processing Systems, volume 2, pages 550--557, San Mateo, CA, 1990. Morgan Kaufmann. (version in postscript)

David H. Ackley and Michael S. Littman. Learning from natural selection in an artificial environment. In Proceedings of the International Joint Conference on Neural Networks, volume 1. Lawrence Erlbaum Associates, Washington DC, January 1990.
The material marked by asterisks (*) is based upon work supported by the National Science Foundation under Grant No. 9702576. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Last update: Sat May 6 09:08:12 EDT 2000