Bayesian abductive inference using overlapping swarm intelligence

Abductive inference in Bayesian networks, is the problem of finding the most likely joint assignment to all non-evidence variables in the network. Such an assignment is called the most probable explanation (MPE). A novel swarm-based algorithm is proposed that finds the k-MPE of a Bayesian network. Our approach is an overlapping swarm intelligence algorithm in which a particle swarm is assigned to each node in the network. Each swarm searches for value assignments for its node's Markov blanket. Swarms that have overlapping value assignments compete to determine which assignment will be used in the final solution. In this paper we compare our algorithm to several other local search algorithms and show that our approach outperforms the competing methods in its ability to find the k-MPE.