Integrating logical and probabilistic reasoning for decision making : Jack Breese Rockwell International Science Center, Palo Alto Laboratory, 444 High Street, Suite 400, Palo Alto, California 94301 Edison Tse Department of Engineering—Economic Systems, Stanford University, Stanford, California 94305

Abstract

We describe a representation and a set of inference methods that combine logic programming techniques with probabilistic network representations for uncertainty (influence diagrams). The techniques emphasize the dynamic construction and solution of probabilistic and decision-theoretic models for complex and uncertain domains. Given a query, a logical proof is produced if possible; if not, an influence diagram based on the query and the knowledge of the decision domain is produced and subsequently solved. A uniform declarative, first-order, knowledge representation is combined with a set of integrated inference procedures for logical, probabilistic, and decision-theoretic reasoning.