An evolutionary optimization approach to cost-based abduction, with comparison to PSO

Abduction is the process of proceeding from data describing a set of observations or events, to a set of hypotheses which best explains or accounts for the data. Cost-based abduction (CBA) is a formalism in which evidence to be explained is treated as a goal to be proven, proofs have costs based on how much needs to be assumed to complete the proof, and the set of assumptions needed to complete the least-cost proof are taken as the best explanation for the given evidence. In this paper, we apply an evolutionary algorithm (EA) to the problem of finding least-cost proofs in cost-based abduction systems, comparing performance to PSO using a difficult problem instance.