Optimization Algorithms to Find Most Similar Deductive Consequences (MSDC)
Identifieur interne : 001069 ( Main/Exploration ); précédent : 001068; suivant : 001070Optimization Algorithms to Find Most Similar Deductive Consequences (MSDC)
Auteurs : Babak Mougouie [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Abstract
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 k most similar deductive consequences k-MSDC.
Url:
DOI: 10.1007/978-3-540-85502-6_25
Affiliations:
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