Multi-Context System for Optimization Problems

被引：0

作者：

Le, Tiep ^{[1
]}

Tran Cao Son ^{[1
]}

Pontelli, Enrico ^{[1
]}

机构：

[1] New Mexico State Univ, Dept Comp Sci, Las Cruces, NM 88003 USA

来源：

THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2019年

关键词：

DISTRIBUTED CONSTRAINT OPTIMIZATION; INCONSISTENCY;

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：

This paper proposes Multi-context System for Optimization Problems (MCS-OP) by introducing conditional cost-assignment bridge rules to Multi-context Systems (MCS). This novel feature facilitates the definition of a preorder among equilibria, based on the total incurred cost of applied bridge rules. As an application of MCS-OP, the paper describes how MCS-OP can be used in modeling Distributed Constraint Optimization Problems (DCOP), a prominent class of distributed optimization problems that is frequently employed in multi-agent system (MAS) research. The paper shows, by means of an example, that MCS-OP is more expressive than DCOP, and hence, could potentially be useful in modeling distributed optimization problems which cannot be easily dealt with using DCOPs. It also contains a complexity analysis of MCS-OP.

引用

页码：2929 / 2937

页数：9

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