Finding least cost proofs using a hierarchical PSO

被引:0
作者
Chivers, Shawn T. [1 ]
Tagliarini, Gene A. [1 ]
Abdelbar, Ashraf M. [2 ]
机构
[1] Univ N Carolina, Dept Comp Sci, Wilmington, NC 28401 USA
[2] Amer Univ Cairo, Dept Comp Sci, Cairo, Egypt
来源
2007 IEEE SWARM INTELLIGENCE SYMPOSIUM | 2007年
关键词
PARTICLE SWARM OPTIMIZATION; ALGORITHM; NETWORKS;
D O I
10.1109/SIS.2007.368040
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Abduction is the process of proceeding from data describing a set of observations or events, to a set of hypotheses which best explains or accounts for the data. Cost-based abduction (CBA) is a formalism in which evidence to be explained is treated as a goal to be proven, proofs have costs based on how much needs to be assumed to complete the proof, and the set of assumptions needed to complete the least-cost proof are taken as the best explanation for the given evidence. In this paper, we explore using a hierarchical PSO to find least-cost proofs in cost-based abduction systems, comparing performance to simulated annealing using a difficult problem instance.
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页码:156 / +
页数:3
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