{"title":"A/sup 2/: an agent-oriented programming architecture for multi-agent constraint satisfaction problems","authors":"E. Freeman","doi":"10.1109/TAI.1990.130446","DOIUrl":null,"url":null,"abstract":"An agent-oriented programming metaphor is used to extend the analytic capabilities of a constraint logic programming system, such as CLP(R), to the domain of multi-agent constraint satisfaction problems. The resulting implementation provides a set of system primitives, which support at a rudimentary level, the maintenance of private knowledge bases, inter-agent communications, constraint driven multi-agent consensus formation, functional inheritance via 'cloning' and a choice of inheritance lattice search optimization mechanisms, allowing knowledge engineers to make speed vs. flexibility and functional dependence vs. independence trade-offs. A general architecture for agent-oriented programming systems is presented, and some of the more salient aspects of its CLP(R) implementation are summarized.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1990-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1990.130446","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
An agent-oriented programming metaphor is used to extend the analytic capabilities of a constraint logic programming system, such as CLP(R), to the domain of multi-agent constraint satisfaction problems. The resulting implementation provides a set of system primitives, which support at a rudimentary level, the maintenance of private knowledge bases, inter-agent communications, constraint driven multi-agent consensus formation, functional inheritance via 'cloning' and a choice of inheritance lattice search optimization mechanisms, allowing knowledge engineers to make speed vs. flexibility and functional dependence vs. independence trade-offs. A general architecture for agent-oriented programming systems is presented, and some of the more salient aspects of its CLP(R) implementation are summarized.<>