Evaluating influence diagrams with decision circuits

TL;DR

This paper introduces decision circuits, a novel method inspired by arithmetic circuits, to efficiently evaluate influence diagrams by exploiting local structure, thereby improving the tractability of complex decision problems.

Contribution

The paper presents decision circuits as a new approach to evaluate influence diagrams, leveraging local structure to enhance computational efficiency over existing algorithms.

Findings

Decision circuits enable efficient influence diagram evaluation.

They exploit local structure for improved performance.

Comparable benefits to arithmetic circuits in Bayesian networks.

Abstract

Although a number of related algorithms have been developed to evaluate influence diagrams, exploiting the conditional independence in the diagram, the exact solution has remained intractable for many important problems. In this paper we introduce decision circuits as a means to exploit the local structure usually found in decision problems and to improve the performance of influence diagram analysis. This work builds on the probabilistic inference algorithms using arithmetic circuits to represent Bayesian belief networks [Darwiche, 2003]. Once compiled, these arithmetic circuits efficiently evaluate probabilistic queries on the belief network, and methods have been developed to exploit both the global and local structure of the network. We show that decision circuits can be constructed in a similar fashion and promise similar benefits.