Hybrid Processing of Beliefs and Constraints

TL;DR

This paper introduces a flexible variable elimination algorithm that integrates probabilistic belief networks with deterministic boolean constraints, improving the evaluation of complex beliefs and queries.

Contribution

It presents a novel parameterized algorithm that combines belief networks and boolean constraints, allowing adjustable constraint propagation levels for more efficient inference.

Findings

Constraint propagation enhances probabilistic computation.

Algorithm's complexity is managed by augmented graph width.

Preliminary results show improved inference performance.

Abstract

This paper explores algorithms for processing probabilistic and deterministic information when the former is represented as a belief network and the latter as a set of boolean clauses. The motivating tasks are 1. evaluating beliefs networks having a large number of deterministic relationships and2. evaluating probabilities of complex boolean querie over a belief network. We propose a parameterized family of variable elimination algorithms that exploit both types of information, and that allows varying levels of constraint propagation inferences. The complexity of the scheme is controlled by the induced-width of the graph {em augmented} by the dependencies introduced by the boolean constraints. Preliminary empirical evaluation demonstrate the effect of constraint propagation on probabilistic computation.