Integrating Logical and Probabilistic Reasoning for Decision Making

TL;DR

This paper presents a unified approach that combines logical reasoning with probabilistic influence diagrams to handle complex decision-making under uncertainty, enabling dynamic model construction and solution.

Contribution

It introduces a novel integrated framework that merges logic programming with probabilistic models for improved decision-making in uncertain domains.

Findings

Effective dynamic construction of influence diagrams from logical queries

Unified inference procedures for logical, probabilistic, and decision reasoning

Demonstrated applicability to complex uncertain decision problems

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.