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首页> 外文会议>International Workshop on Computational Logic in Multi-Agent Systems(CLIMA IV); 20040106-07; Fort Lauderdale,FL(US) >Learning in BDI Multi-agent Systems
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Learning in BDI Multi-agent Systems

机译：在BDI多主体系统中学习

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摘要

This paper deals with the issue of learning in multi-agent systems (MAS). Particularly, we are interested in BDI (Belief, Desire, Intention) agents. Despite the relevance of the BDI model of rational agency, little work has been done to deal with its two main limitations: ⅰ) The lack of learning competences; and ⅱ) The lack of explicit multi-agent functionality. From the multi-agent learning perspective, we propose a BDI agent architecture extended with learning competences for MAS context. Induction of Logical Decision Trees, a first order method, is used to enable agents to learn when their plans are successfully executable. Our implementation enables multiple agents executed as parallel functions in a single Lisp image. In addition, our approach maintains consistency between learning and the theory of practical reasoning.
机译：本文讨论了多智能体系统（MAS）中的学习问题。特别是，我们对BDI（信念，愿望，意图）代理商感兴趣。尽管理性代理人的BDI模型具有相关性，但为解决其两个主要局限性，仍未开展任何工作：ⅰ）学习能力不足； ⅱ）缺乏明确的多主体功能。从多主体学习的角度，我们提出了一种BDI代理架构，该架构扩展了针对MAS上下文的学习能力。逻辑决策树的归纳是一种一阶方法，用于使代理能够了解其计划何时可以成功执行。我们的实现使多个代理可以在单个Lisp映像中作为并行函数执行。此外，我们的方法保持了学习与实践推理理论之间的一致性。

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