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Optimization Algorithms to Find Most Similar Deductive Consequences (MSDC)

Identifieur interne : 001826 ( Istex/Corpus ); précédent : 001825; suivant : 001827

Optimization Algorithms to Find Most Similar Deductive Consequences (MSDC)

Auteurs : Babak Mougouie

Source :

RBID : ISTEX:AA6E9AB10754DD96B556C05DFADC6542F20740B6

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

Links to Exploration step

ISTEX:AA6E9AB10754DD96B556C05DFADC6542F20740B6

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<abstract lang="en">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.</abstract>
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