Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming

Thiago P. Bueno, Denis D. Mauá, Leliane N. De Barros, Fabio G. Cozman

Research output: Contribution to conferenceConference Paper

1 Scopus citations

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 languageAmerican English
Pages49-60
Number of pages12
StatePublished - 1 Jan 2019
Externally publishedYes
EventProceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017 -
Duration: 1 Jan 2019 → …

Conference

ConferenceProceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017
Period1/01/19 → …

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