AAAI Publications, Twenty-First International Joint Conference on Artificial Intelligence

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Evaluating Abductive Hypotheses using an EM Algorithm on BDDs
Katsumi Inoue, Taisuke Sato, Masakazu Ishihata, Yoshitaka Kameya, Hidetomo Nabeshima

Last modified: 2009-06-25


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.


abduction; hypothesis generation; hypothesis evaluation; EM algorithm; binary decision diagrams; consequence finding; systems biology

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