nips nips2008 nips2008-211 nips2008-211-reference knowledge-graph by maker-knowledge-mining
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Author: Erik Talvitie, Satinder P. Singh
Abstract: We present a novel mathematical formalism for the idea of a “local model” of an uncontrolled dynamical system, a model that makes only certain predictions in only certain situations. As a result of its restricted responsibilities, a local model may be far simpler than a complete model of the system. We then show how one might combine several local models to produce a more detailed model. We demonstrate our ability to learn a collection of local models on a large-scale example and do a preliminary empirical comparison of learning a collection of local models and some other model learning methods. 1
[1] Lise Getoor, Nir Friedman, Daphne Koller, and Benjamin Taskar. Learning probabilistic models of relational structure. Journal of Machine Learning Research, 3:679–707, 2002.
[2] Zoubin Ghahramani and Michael I. Jordan. Factorial hidden Markov models. In Advances in Neural Information Processing Systems 8 (NIPS), pages 472–478, 1995.
[3] Hanna M. Pasula, Luke S. Zettlemoyer, and Leslie Pack Kaelbling. Learning symbolic models of stochastic domains. Journal of Artificial Intelligence, 29:309–352, 2007.
[4] Michael Littman, Richard Sutton, and Satinder Singh. Predictive representations of state. In Advances in Neural Information Processing Systems 14 (NIPS), pages 1555–1561, 2002.
[5] Herbert Jaeger. Observable operator models for discrete stochastic time series. Neural Computation, 12(6):1371–1398, 2000.
[6] Satinder Singh, Michael R. James, and Matthew R. Rudary. Predictive state representations: A new theory for modeling dynamical systems. In Uncertainty in Artificial Intelligence 20 (UAI), pages 512–519, 2004.
[7] Richard Sutton, Doina Precup, and Satinder Singh. Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence, 112:181–211, 1999.
[8] Alicia Peregrin Wolfe and Andrew G. Barto. Decision tree methods for finding reusable MDP homomorphisms. In National Conference on Artificial Intelligence 21 (AAAI), 2006.
[9] Vishal Soni and Satinder Singh. Abstraction in predictive state representations. In National Conference on Artificial Intelligence 22 (AAAI), 2007.
[10] Erik Talvitie, Britton Wolfe, and Satinder Singh. Building incomplete but accurate models. In International Symposium on Artificial Intelligence and Mathematics (ISAIM), 2008.
[11] George E. Monahan. A survey of partially observable markov decisions processes: Theory, models, and algorithms. Management Science, 28(1):1–16, 1982.
[12] Craig Boutilier, Nir Friedman, Moises Goldszmidt, and Daphne Koller. Context-specific independence in bayesian networks. In Uncertainty in Artificial Intelligence 12 (UAI), pages 115–123, 1996.
[13] Britton Wolfe, Michael James, and Satinder Singh. Approximate predictive state representations. In Autonomous Agents and Multiagent Systems 7 (AAMAS), 2008.
[14] Adam Berger, Stephen Della Pietra, and Vincent Della Pietra. A maximum entropy approach to natural language processing. Computational Linguistics, 22(1):39–71, 1996.
[15] David Wingate and Satinder Singh. Exponential family predictive representations of state. In Advances in Neural Information Processing Systems 20 (NIPS), pages 1617–1624, 2007.
[16] Jeff Bilmes. The graphical models toolkit (gmtk), 2007. http://ssli.ee.washington.edu/ ˜bilmes/gmtk.
[17] Michael James and Satinder Singh. Learning and discovery of predictive state representations in dynamical systems with reset. In International Conference on Machine Learning 21 (ICML), 2004. 8