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Planning Under Uncertainty
The focus of this research project is on planning under uncertainty using Markov decision processes. The main application areas is the design of automated planning and control systems for stochastic domains including mobile robotics. The theoretical emphasis is on algorithms for solving Markov decision processes with very large state and action spaces.
Project status: Active
Research Areas
| Artificial Intelligence |
Research Themes
| Statistical Approaches |
| Machine Decision and Game Theory |
| Applications to Medicine |
People
| Thomas Dean |
Publications
Givan, R., Dean, T., and Greig, M. Equivalence Notions and Model Minimization in Markov Decision Processes. Artificial Intelligence 147, 1-2 (2003), 163-223. [ pdf ]
Kim, K.-E., and Dean, T. Solving Factored Markov Decision Processes Using Non-homogeneous Partitions. Artificial Intelligence 147, 1-2 (2003), 225-251. [ pdf ]
Givan, R., Leach, S., and Dean, T. Bounded Parameter Markov Decision Processes. Artificial Intelligence 122, 1-2 (2000), 71-109. [ pdf ]
Boutilier, C., Dean, T., and Hanks, S. Decision Theoretic Planning: Structural Assumptions and Computational Leverage. Journal of Artificial Intelligence Research 11 (1999), 1-94. [ pdf ]
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