GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks

TL;DR

This paper introduces GALGO, a genetic algorithm-based decision support tool designed to perform approximate abductive inference efficiently in large, complex Bayesian belief networks where exact methods are computationally infeasible.

Contribution

The paper presents a novel genetic algorithm approach for abductive inference in complex belief networks, addressing NP-hard challenges and scalability issues.

Findings

GALGO provides efficient approximate inference in large networks.

Preliminary results show promising accuracy and computational advantages.

The method is theoretically justified for complex, multiply connected networks.

Abstract

Bayesian belief networks can be used to represent and to reason about complex systems with uncertain, incomplete and conflicting information. Belief networks are graphs encoding and quantifying probabilistic dependence and conditional independence among variables. One type of reasoning of interest in diagnosis is called abductive inference (determination of the global most probable system description given the values of any partial subset of variables). In some cases, abductive inference can be performed with exact algorithms using distributed network computations but it is an NP-hard problem and complexity increases drastically with the presence of undirected cycles, number of discrete states per variable, and number of variables in the network. This paper describes an approximate method based on genetic algorithms to perform abductive inference in large, multiply connected networks for which complexity is a concern when using most exact methods and for which systematic search methods are not feasible. The theoretical adequacy of the method is discussed and preliminary experimental results are presented.