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Factored Models for Probabilistic Modal Logic Afsaneh Shirazi and Eyal Amir

Summary: Factored Models for Probabilistic Modal Logic
Afsaneh Shirazi and Eyal Amir
Computer Science Department, University of Illinois at U­C
Urbana, IL 61801, USA {hajiamin, eyal}@uiuc.edu
Modal logic represents knowledge that agents have about
other agents' knowledge. Probabilistic modal logic fur­
ther captures probabilistic beliefs about probabilistic beliefs.
Models in those logics are useful for understanding and de­
cision making in conversations, bargaining situations, and
competitions. Unfortunately, probabilistic modal structures
are impractical for large real­world applications because they
represent their state space explicitly. In this paper we scale
up probabilistic modal structures by giving them a factored
representation. This representation applies conditional inde­
pendence for factoring the probabilistic aspect of the structure
(as in Bayesian Networks (BN)). We also present two exact
and one approximate algorithm for reasoning about the truth
value of probabilistic modal logic queries over a model en­
coded in a factored form. The first exact algorithm applies


Source: Amir, Eyal - Department of Computer Science, University of Illinois at Urbana-Champaign


Collections: Computer Technologies and Information Sciences