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|Title:||Probabilistic goal Markov decision processes|
|Authors:||Xu, H. |
|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|
|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.|
|Source Title:||IJCAI International Joint Conference on Artificial Intelligence|
|Appears in Collections:||Staff Publications|
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