Abstract. When a model does not satisfy a given specification, a counterexample is produced by the model checker to demonstrate the failure. A user must then examine the counterexa...
Ilan Beer, Shoham Ben-David, Hana Chockler, Avigai...
There has been substantial progress with formal models for sequential decision making by individual agents using the Markov decision process (MDP). However, similar treatment of m...
Raphen Becker, Shlomo Zilberstein, Victor R. Lesse...
Loopy and generalized belief propagation are popular algorithms for approximate inference in Markov random fields and Bayesian networks. Fixed points of these algorithms have been...
There is increasing interest within the research community in the design and use of recursive probability models. There remains concern about computational complexity costs and th...
Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...