Optimization Algorithms to Find Most Similar Deductive Consequences (MSDC)

Authors:
Babak Mougouie
Affiliations:
Department of Business Information Systems II, University of Trier, Trier, Germany and DFKI GmbH, Knowledge Management Department, Kaiserslautern, Germany
Venue:
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Year:
2008

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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 formulation $\mathcal{OP}$-MSDC(q) of MSDC which is a mixed integer optimization problem. Although mixed integer optimization problems are exponentially solvable in general, our experimental results show that $\mathcal{OP}$-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 kmost similar deductive consequences k-MSDC.