Paper Info
Title
Probabilistic Knowledge-Based Programs.
Abstract
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.
Year
Venue
DocType
2015
IJCAI
Conference
Citations 
PageRank 
References 
0
0.34
24
Authors
2
Name
Order
Citations
PageRank
Jérôme Lang12838260.90
Bruno Zanuttini228925.43