Partial abductive inference in Bayesian belief networks using a genetic algorithm

Abductive inference in Bayesian belief networks is the process of generatin g the K most probable configurations given an observed evidence. When we ar e only interested in a subset of the network's variables, this problem is c alled partial abductive inference. Both problems are NP-hard, and so exact computation is not always possible. This paper describes an approximate met hod based on genetic algorithms to perform partial abductive inference. We have tested the algorithm using the alarm network and from the experimental results we can conclude that the algorithm presented here is a good tool t o perform this kind of probabilistic reasoning. (C) 1999 Elsevier Science B .V. All rights reserved.