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.
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© 2004 Springer-Verlag Berlin Heidelberg
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Guo, H., Boddhireddy, P.R., Hsu, W.H. (2004). An ACO Algorithm for the Most Probable Explanation Problem. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_67
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DOI: https://doi.org/10.1007/978-3-540-30549-1_67
Publisher Name: Springer, Berlin, Heidelberg
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