
Probabilistic Knowledge-Based Programs
Lang, Jérôme; Zanuttini, Bruno (2015), Probabilistic Knowledge-Based Programs, in Yang, Qiang; Wooldridge, Michael, Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), AAAI Press / IJCAI : Palo Alto (USA), p. 1594-1600
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Type
Communication / ConférenceDate
2015Conference title
24th International Joint Conference on Artificial Intelligence (IJCAI 2015)Conference date
2015-07Conference city
Buenos AiresConference country
ArgentinaBook title
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)Book author
Yang, Qiang; Wooldridge, MichaelPublisher
AAAI Press / IJCAI
Published in
Palo Alto (USA)
ISBN
978-1-57735-738-4
Number of pages
4429Pages
1594-1600
Metadata
Show full item recordAuthor(s)
Lang, JérômeLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Zanuttini, Bruno
Groupe de Recherche en Informatique, Image et Instrumentation de Caen [GREYC]
Abstract (EN)
We introduce Probabilistic Knowledge-Based Programs (PKBPs), a new, compact representation of policies for factored partially observable Markov decision processes. PKBPs use branching conditions such as if the probability of ϕ is larger than p, and many more. While similar in spirit to value-based policies, PKBPs leverage the factored representation for more compactness. They also cope with more general goals than standard state-based rewards, such as pure information-gathering goals. Compactness comes at the price of reactivity, since evaluating branching conditions on-line is not polynomial in general. In this sense, PKBPs are complementary to other representations. Our intended application is as a tool for experts to specify policies in a natural, compact language, then have them verified automatically. We study succinctness and the complexity of verification for PKBPs.Subjects / Keywords
Planning; Artificial IntelligenceRelated items
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