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 Seiten 370-384 Lecture Notes in Artificial Intelligence (LNAI) 5239 Springer 2008.

Abstrakt

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.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence