Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
2-2016
Abstract
Partially Observable Markov Decision Processes (POMDPs) are often used to model planning problems under uncertainty. The goal in Risk-Sensitive POMDPs (RS-POMDPs) is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. In this paper, unlike existing POMDP literature, we distinguish between the two cases of whether costs can or cannot be observed and show the empirical impact of cost observations. We also introduce a new search-based algorithm to solve RS-POMDPs and show that it is faster and more scalable than existing approaches in two synthetic domains and a taxi domain generated with real-world data.
Discipline
Theory and Algorithms
Publication
Proceedings of the AAAI Conference on Artificial Intelligence 2016: Phoenix Arizona, February 12-17, 2016
Publisher
AAAI Press
City or Country
Menlo Park, CA
Citation
HOU, Ping; YEOH, William; and Pradeep VARAKANTHAM.
Solving risk-sensitive POMDPs with and without cost observations. (2016). Proceedings of the AAAI Conference on Artificial Intelligence 2016: Phoenix Arizona, February 12-17, 2016.
Available at: https://ink.library.smu.edu.sg/sis_research/3605
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.