Cost - Based Abduction , Constraint Systems and AlternativeGeneration

Cost-based abduction attempts to prove an observation by assuming some set of hypotheses. Hypotheses have associated cost and the best proof is the one which assumes the least costly set. Previous approaches have formalized minimum cost-based abduction as a heuristic search problem. However, eecient admissible heuristics seem to be diicult to nd. In this paper, we present an approach using linear constraints to represent causal relationships providing a new framework for modeling cost-based abduction. Moreover, we are able to utilize the highly eecient optimization tools of operations research, thus yielding a computationally eecient method for solving cost-based abduction problems. Experimentation suggests that our approach is superior to existing search techniques in that our approach exhibits a slower run-time growth rate. This formulation also permits the natural generation of alternative explanations .