Counting-Based Search for Constraint Optimization Problems

被引：0

作者：

Pesant, Gilles ^{[1
,2
]}

机构：

[1] Ecole Polytech Montreal, Montreal, PQ, Canada

[2] Univ Montreal, CIRRELT, Montreal, PQ, Canada

来源：

THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2016年

基金：

加拿大自然科学与工程研究理事会;

关键词：

HEURISTICS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Branching heuristics based on counting solutions in constraints have been quite good at guiding search to solve constraint satisfaction problems. But do they perform as well for constraint optimization problems? We propose an adaptation of counting-based search for optimization, show how to modify solution density computation for some of the most frequently-occurring constraints, and empirically evaluate its performance on several benchmark problems.

引用

页码：3441 / 3447

页数：7

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