Uncertainty in Artificial Intelligence
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Symbolic Dynamic Programming for Discrete and Continuous State MDPs
Scott Sanner, Karina Valdivia Delgado, Leliane Nunes de Barros
Many real-world decision-theoretic planning problems can be naturally modeled with discrete and continuous state Markov decision processes (DC-MDPs). While previous work has addressed automated decision-theoretic planning for DCMDPs, optimal solutions have only been defined so far for limited settings, e.g., DC-MDPs having hyper-rectangular piecewise linear value functions. In this work, we extend symbolic dynamic programming (SDP) techniques to provide optimal solutions for a vastly expanded class of DCMDPs. To address the inherent combinatorial aspects of SDP, we introduce the XADD - a continuous variable extension of the algebraic decision diagram (ADD) - that maintains compact representations of the exact value function. Empirically, we demonstrate an implementation of SDP with XADDs on various DC-MDPs, showing the first optimal automated solutions to DCMDPs with linear and nonlinear piecewise partitioned value functions and showing the advantages of constraint-based pruning for XADDs.
Pages: 643-652
PS Link:
PDF Link: /papers/11/p643-sanner.pdf
AUTHOR = "Scott Sanner and Karina Valdivia Delgado and Leliane Nunes de Barros",
TITLE = "Symbolic Dynamic Programming for Discrete and Continuous State MDPs",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "643--652"

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