Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2014

Abstract

While Markov Decision Processes (MDPs) have been shown to be effective models for planning under uncertainty, theobjective to minimize the expected cumulative cost is inappropriate for high-stake planning problems. As such, Yu, Lin, and Yan (1998) introduced the Risk-Sensitive MDP (RSMDP) model, where the objective is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. In this paper, we revisit this problem and introduce new algorithms that are based on classical techniques, such as depth-first search and dynamic programming, and a recently introduced technique called Topological Value Iteration (TVI). We demonstrate the applicability of our approach on randomly generated MDPs as well as domains from the ICAPS 2011 International Probabilistic Planning Competition (IPPC).

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Publication

Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS-14): 21-26 June 2014, Portsmouth, New Hampshire

First Page

136

Last Page

144

Publisher

AAAI Press

City or Country

Menlo Park, CA

Additional URL

http://aaai.org/ocs/index.php/ICAPS/ICAPS14/paper/view/7798

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