An ACO algorithm for the most probable explanation problem

Authors:
Haipeng Guo;Prashanth R. Boddhireddy;William H. Hsu
Affiliations:
Department of Computer Science, Hong Kong University of Science and Technology;Department of Plant Pathology;Department of Computing and Information Sciences, Kansas State University
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

We describe an Ant Colony Optimization (ACO) algorithm, ANT-MPE, for the most probable explanation problem in Bayesian network inference After tuning its parameters settings, we compare ANT-MPE with four other sampling and local search-based approximate algorithms: Gibbs Sampling, Forward Sampling, Multistart Hillclimbing, and Tabu Search Experimental results on both artificial and real networks show that in general ANT-MPE outperforms all other algorithms, but on networks with unskewed distributions local search algorithms are slightly better The result reveals the nature of ACO as a combination of both sampling and local search It helps us to understand ACO better, and, more important, it also suggests a possible way to improve ACO.