Evaluating Abductive Hypotheses using an EM Algorithm on BDDs
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 810-814.
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.Katsumi Inoue, Taisuke Sato, Masakazu Ishihata, Yoshitaka Kameya, Hidetomo Nabeshima
@inproceedings{ijcai2009p139,
title = {Evaluating Abductive Hypotheses using an EM Algorithm on BDDs},
author = {Inoue, Katsumi and Sato, Taisuke and Ishihata, Masakazu and Kameya, Yoshitaka and Nabeshima, Hidetomo},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {810--814},
year = {2009},
url = {https://ijcai.org/proceedings/2009/139},
}
TY - CPAPER TI - Evaluating Abductive Hypotheses using an EM Algorithm on BDDs AU - Inoue, Katsumi AU - Sato, Taisuke AU - Ishihata, Masakazu AU - Kameya, Yoshitaka AU - Nabeshima, Hidetomo T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 810 EP - 814 UR - https://ijcai.org/proceedings/2009/139 ER -Copied
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