Evaluating abductive hypotheses using an EM algorithm on BDDs

Authors:
Katsumi Inoue;Taisuke Sato;Masakazu Ishihata;Yoshitaka Kameya;Hidetomo Nabeshima
Affiliations:
Principles of Informatics Research Division, National Institute of Informatics, Japan and Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Japan;Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Japan and Principles of Informatics Research Division, National Institute of Informatics, Japan;Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Japan;Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Japan;Division of Medicine and Engineering Science, University of Yamanashi, Japan
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

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Abstract

Abductive inference is an important AI reasoning technique to find explanations of observations, and has recently been applied to scientific discovery. To find best hypotheses among many logically possible hypotheses, we need to evaluate hypotheses obtained from the process of hypothesis generation. We propose an abductive inference architecture combined with an EM algorithm working on binary decision diagrams (BDDs). This work opens a way of applying BDDs to compress multiple hypotheses and to select most probable ones from them. An implemented system has been applied to inference of inhibition in metabolic pathways in the domain of systems biology.