Skip to main content Skip to main navigation

Publikation

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.

Zusammenfassung

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.