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Communication Dans Un Congrès Année : 2015

Probabilistic Knowledge-Based Programs

Résumé

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.
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Dates et versions

hal-01196656 , version 1 (10-09-2015)

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  • HAL Id : hal-01196656 , version 1

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Jérôme Lang, Bruno Zanuttini. Probabilistic Knowledge-Based Programs . 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Jul 2015, Buenos Aires, Argentina. ⟨hal-01196656⟩
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