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Abductive inference in Bayesian networks using distributed overlapping swarm intelligence

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Abstract

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 \(k\) most probable state assignments to all non-evidence variables in the network while partial abductive inference is the problem of finding the \(k\) 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.

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Acknowledgments

The authors would like to thank the members of the Numerical Intelligent Systems Laboratory at Montana State University for their comments and advise during the development of this work. We would also like to thank Dr. Brian Haberman at the Johns Hopkins University Applied Physics Laboratory and Karthik Ganesan Pillai in the Data Mining Laboratory at MSU for their ideas during the formative stages of this research.

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Authors and Affiliations

  1. Department of Computer Science, Montana State University, Bozeman, MT, 59717, USA

    Nathan Fortier, John Sheppard & Shane Strasser

Authors
  1. Nathan Fortier
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  2. John Sheppard
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  3. Shane Strasser
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Correspondence to Nathan Fortier.

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Communicated by A. Castiglione.

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Fortier, N., Sheppard, J. & Strasser, S. Abductive inference in Bayesian networks using distributed overlapping swarm intelligence. Soft Comput 19, 981–1001 (2015). https://doi.org/10.1007/s00500-014-1310-0

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  • DOI: https://doi.org/10.1007/s00500-014-1310-0

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