Integrating Logical and Probabilistic Reasoning for Decision Making
This work addresses decision-making challenges in AI by combining logic programming with probabilistic networks, though it appears incremental as it builds on existing techniques.
The paper tackles the problem of decision-making in complex and uncertain domains by integrating logical and probabilistic reasoning, resulting in a system that dynamically constructs and solves influence diagrams for queries when logical proofs are not possible.
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.