||April 23, 2009
||Prof. Katsumi Inoue, National Institute of Informatics, Japan
||Evaluating Abductive Hypotheses using an EM Algorithm on BDDs
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.
(This work will be presented at IJCAI-2009, Pasadena, CA, July 2009.)