caa a22 4500 605469989 CHVBK 20210128100325.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00500-014-1310-0 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1310-0 Abductive inference in Bayesian networks using distributed overlapping swarm intelligence [Elektronische Daten] [Nathan Fortier, John Sheppard, Shane Strasser] In this paper we propose several approximation algorithms for the problems of full and partial abductive inference in Bayesian belief networks. Full abductive inference is the problem of finding the $$k$$ k most probable state assignments to all non-evidence variables in the network while partial abductive inference is the problem of finding the $$k$$ k most probable state assignments for a subset of the non-evidence variables in the network, called the explanation set. We developed several multi-swarm algorithms based on the overlapping swarm intelligence framework to find approximate solutions to these problems. For full abductive inference a swarm is associated with each node in the network. For partial abductive inference, a swarm is associated with each node in the explanation set and each node in the Markov blankets of the explanation set variables. Each swarm learns the value assignments for the variables in the Markov blanket associated with that swarm's node. Swarms learning state assignments for the same variable compete for inclusion in the final solution. Springer-Verlag Berlin Heidelberg, 2014 Abductive inference nationallicence Particle swarm optimization nationallicence Distributed computing nationallicence Fortier Nathan Department of Computer Science, Montana State University, 59717, Bozeman, MT, USA aut Sheppard John Department of Computer Science, Montana State University, 59717, Bozeman, MT, USA aut Strasser Shane Department of Computer Science, Montana State University, 59717, Bozeman, MT, USA aut Soft Computing Springer Berlin Heidelberg 19/4(2015-04-01), 981-1001 1432-7643 19:4<981 2015 19 500 https://doi.org/10.1007/s00500-014-1310-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1310-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fortier Nathan Department of Computer Science, Montana State University, 59717, Bozeman, MT, USA aut NATIONALLICENCE 700 1- Sheppard John Department of Computer Science, Montana State University, 59717, Bozeman, MT, USA aut NATIONALLICENCE 700 1- Strasser Shane Department of Computer Science, Montana State University, 59717, Bozeman, MT, USA aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/4(2015-04-01), 981-1001 1432-7643 19:4<981 2015 19 500