Abstract
Copyright © PMLR 2017. All rights reserved. We study languages that specify Markov Decision Processes with Imprecise Probabilities (MDPIPs) by mixing probabilities and logic programming. We propose a novel language that can capture MDPIPs and Markov Decision Processes with Set-valued Transitions (MDPSTs); we then obtain the complexity of one-step inference for the resulting MDPIPs and MDPSTs. We also present results of independent interest on the complexity of inference with probabilistic logic programs containing interval-valued probabilistic assessments. Finally, we also discuss policy generation techniques.
Original language | American English |
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Pages | 49-60 |
Number of pages | 12 |
State | Published - 1 Jan 2019 |
Externally published | Yes |
Event | Proceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017 - Duration: 1 Jan 2019 → … |
Conference
Conference | Proceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017 |
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Period | 1/01/19 → … |