Publication
Optimization Algorithms to Find Most Similar Deductive Consequences (MSDC)
Babak Mougouie
In: K.-D. Althoff; R. Bergmann; M. Minor; A. Hanft (Hrsg.). Proceedings of the 9th European Conference on Case-Based Reasoning. European Conference on Case-Based Reasoning (ECCBR-2008), September 1-4, Trier, Germany, Pages 370-384, Lecture Notes in Artificial Intelligence (LNAI), Vol. 5239, Springer, 2008.
Abstract
Finding most similar deductive consequences, MSDC, is a
new approach which builds a unified framework to integrate similarity-
based and deductive reasoning. In this paper we introduce a new formula-
tion OP-MSDC(q) of MSDC which is a mixed integer optimization prob-
lem. Although mixed integer optimization problems are exponentially
solvable in general, our experimental results show that OP-MSDC(q)
is surprisingly solved faster than previous heuristic algorithms. Based
on this observation we expand our approach and propose optimization
algorithms to find the k most similar deductive consequences k-MSDC.