  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.
