Towards Mining for Influence in a Multi Agent Environment
Towards Mining for Influence in a Multi Agent Environment
复制标题
在多主体环境中挖掘影响力
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
K. Waugh
中科院分区：
文献类型：
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作者：
R. Logie;Jon G. Hall;K. Waugh
多智能体学习系统提出了一组有趣的问题：在大型环境中，智能体可能会发展出不一定是最优的局部行为模式；在纯智能体系统中，没有全局感知的元素可以识别和消除倒退行为；随着系统的规模扩大，它们可能产生大量数据，一个系统可能有大约106个单元和105个智能体，每个单元产生大量数据。这篇立场论文介绍了一项研究，该研究将数据挖掘与逻辑框架相结合，使大型系统中的代理能够了解他们的环境，并制定适合于满足系统规范的行为。我们在传统的多智能体系统的基础上建立起来，使用数据挖掘技术将一种新的进程代数方法添加到合作中，以识别值得学习的合作行为。其结果被预测为一个学习系统，在这个学习系统中，代理人组成集体，增加他们对环境的“相互影响”。
Multi agent learning systems pose an interesting set of problems: in large environments agents may develop localised behaviour patterns that are not necessarily optimal; in a pure agent system there is no globally aware element which can identify and eliminate retrograde behaviour; and as systems scale they may produce large amounts of data, a system may have in the order of 106 cells with 105 agents, each generating large amounts of data. This position paper introduces research that combines data mining with a logical framework to allow agents in large systems to learn about their environment and develop behaviours appropriate to satisfying system norms. We build from traditional multi agent systems, adding a novel process algebraic approach to co-operation using data mining techniques to identify co-operative behaviours worth learning. The result is predicted to be a learning system in which agents form collectives increasing their ‘mutual influence’ on the environment.