Paper Info
Title
Optimization Algorithms to Find Most Similar Deductive Consequences (MSDC)
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.
Year
DOI
Venue
2008
10.1007/978-3-540-85502-6_25
ECCBR
Keywords
Field
DocType
previous heuristic algorithm,optimization algorithm,similar deductive consequences,optimization algorithms,new formulation,kmost similar deductive consequence,deductive reasoning,mixed integer optimization problem,similar deductive consequence,exponentially solvable,new approach,optimization problem,heuristic algorithm
Integer,MSDC,Hill climbing,Heuristic,Mathematical optimization,Algebra,Domain theory,Optimization algorithm,Deductive reasoning,Optimization problem,Mathematics
Conference
Volume
ISSN
Citations 
5239
0302-9743
1
PageRank 
References 
Authors
0.40
4
1
Name
Order
Citations
PageRank
Babak Mougouie1686.01