Authors:
Nicolas A. Espinosa Dice
1
;
Megan L. Kaye
1
;
Hana Ahmed
1
;
2
and
George D. Montañez
1
Affiliations:
1
AMISTAD Lab, Department of Computer Science, Harvey Mudd College, Claremont, CA, U.S.A.
;
2
Scripps College, Claremont, CA, U.S.A.
Keyword(s):
Abductive Logic, Machine Learning, Creative Abduction, Creativity, Graphical Model, Bayesian Network, Probabilistic Abduction.
Abstract:
We present an abductive search strategy that integrates creative abduction and probabilistic reasoning to produce plausible explanations for unexplained observations. Using a graphical model representation of abductive search, we introduce a heuristic approach to hypothesis generation, comparison, and selection. To identify creative and plausible explanations, we propose 1) applying novel structural similarity metrics to a search for simple explanations, and 2) optimizing for the probability of a hypothesis’ occurrence given known observations.