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https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-341
DC Field | Value | |
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dc.title | Probabilistic goal Markov decision processes | |
dc.contributor.author | Xu, H. | |
dc.contributor.author | Mannor, S. | |
dc.date.accessioned | 2014-06-19T05:39:14Z | |
dc.date.available | 2014-06-19T05:39:14Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | Xu, H., Mannor, S. (2011). Probabilistic goal Markov decision processes. IJCAI International Joint Conference on Artificial Intelligence : 2046-2052. ScholarBank@NUS Repository. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-341 | |
dc.identifier.isbn | 9781577355120 | |
dc.identifier.issn | 10450823 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/73777 | |
dc.description.abstract | The Markov decision process model is a powerful tool in planing tasks and sequential decision making problems. The randomness of state transitions and rewards implies that the performance of a policy is often stochastic. In contrast to the standard approach that studies the expected performance, we consider the policy that maximizes the probability of achieving a pre-determined target performance, a criterion we term probabilistic goal Markov decision processes. We show that this problem is NP-hard, but can be solved using a pseudo-polynomial algorithm. We further consider a variant dubbed "chance-constraint Markov decision problems," that treats the probability of achieving target performance as a constraint instead of the maximizing objective. This variant is NP-hard, but can be solved in pseudo-polynomial time. | |
dc.source | Scopus | |
dc.type | Conference Paper | |
dc.contributor.department | MECHANICAL ENGINEERING | |
dc.description.doi | 10.5591/978-1-57735-516-8/IJCAI11-341 | |
dc.description.sourcetitle | IJCAI International Joint Conference on Artificial Intelligence | |
dc.description.page | 2046-2052 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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