Multilinear and Integer Programming for Markov Decision Processes with Imprecise Probabilities

Ricardo Shirota Filho, Fabio Gagliardi Cozman, Felipe Werndl Trevizan, Cassio Polpo de Campos, Leliane Nunes de Barros

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)
109 Downloads (Pure)

Abstract

Markov Decision Processes (MDPs) are extensively used to encode sequences of decisions with probabilistic effects. Markov Decision Processes with Imprecise Probabilities (MDPIPs) encode sequences of decisions whose effects are modeled using sets of probability distributions. In this paper we examine the computation of Γ-maximin policies for MDPIPs using multilinear and integer programming. We discuss the application of our algorithms to “factored” models and to a recent proposal, Markov Decision Processes with Set-valued Transitions (MDPSTs), that unifies the fields of probabilistic and “nondeterministic” planning in artificial intelligence research. 
Original languageEnglish
Title of host publicationProceedings of the 5th International Symposium on Imprecise Probability: Theories and Applications
Pages395-404
Number of pages10
Publication statusPublished - 2007
EventThe 5th International Symposium on Imprecise Probability: Theories and Applications - Prague, Czech Republic
Duration: 16 Jul 200719 Jul 2007

Conference

ConferenceThe 5th International Symposium on Imprecise Probability: Theories and Applications
CountryCzech Republic
CityPrague
Period16/07/200719/07/2007

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