Behavioral flexibility in Belief-Desire- Intention (BDI) architectures

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS Multiagent and Grid Systems Pub Date : 2020-01-01 DOI:10.3233/mgs-200335
Adel Saadi, R. Maamri, Z. Sahnoun
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引用次数: 2

Abstract

The Belief-Desire-Intention (BDI) model is a popular approach to design flexible agents. The key ingredient of BDI model, that contributed to concretize behavioral flexibility, is the inclusion of the practical reasoning. On the other hand, researchers signaled some missing flexibility’s ingredient, in BDI model, essentially the lack of learning. Therefore, an extensive research was conducted in order to extend BDI agents with learning. Although this latter body of research is important, the key contribution of BDI model, i.e., practical reasoning, did not receive a sufficient attention. For instance, for performance reasons, some of the concepts included in the BDI model are neglected by BDI architectures. Neglecting these concepts was criticized by some researchers, as the ability of the agent to reason will be limited, which eventually leads to a more or less flexible reasoning, depending on the concepts explicitly included. The current paper aims to stimulate the researchers to re-explore the concretization of practical reasoning in BDI architectures. Concretely, this paper aims to stimulate a critical review of BDI architectures regarding the flexibility, inherent from the practical reasoning, in the context of single agents, situated in an environment which is not associated with uncertainty. Based on this review, we sketch a new orientation and some suggested improvements for the design of BDI agents. Finally, a simple experiment on a specific case study is carried out to evaluate some suggested improvements, namely the contribution of the agent’s “well-informedness” in the enhancement of the behavioral flexibility.
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信念-欲望-意图(BDI)架构中的行为灵活性
信念-欲望-意图(Belief-Desire-Intention, BDI)模型是设计柔性智能体的常用方法。BDI模型的关键因素是包含了实践推理,这有助于将行为灵活性具体化。另一方面,研究人员指出,在BDI模型中,缺少一些灵活性的成分,本质上是缺乏学习。因此,为了扩展BDI代理的学习能力,进行了广泛的研究。虽然后一项研究很重要,但BDI模型的关键贡献,即实践推理,并没有得到足够的重视。例如,出于性能原因,BDI体系结构忽略了BDI模型中包含的一些概念。忽视这些概念受到一些研究人员的批评,因为智能体的推理能力将受到限制,最终导致或多或少的灵活推理,取决于明确包含的概念。本文旨在激发研究者重新探索BDI架构中实践推理的具体化。具体地说,本文旨在激发对BDI架构关于灵活性的批判性审查,这是在单个代理的背景下,从实际推理中固有的,位于与不确定性无关的环境中。在此基础上,提出了BDI制剂设计的新方向和改进建议。最后,通过一个简单的案例研究来评估一些建议的改进,即agent的“良好信息”在增强行为灵活性方面的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Multiagent and Grid Systems
Multiagent and Grid Systems COMPUTER SCIENCE, THEORY & METHODS-
CiteScore
1.50
自引率
0.00%
发文量
13
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