A reasoning system about knowledge acquisition in bi-agent interaction

Jinsheng Gao, Changle Zhou
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引用次数: 2

Abstract

In artificial intelligence, knowledge acquisition and reasoning are the most basic key elements for intelligent agent to obtain the ability of the simulation thinking which capacitate intelligent agent to carry out a series of actions. At present, Dynamic Epistemic Logic (DEL) has been a primary technique for modelling knowledge acquisition and reasoning. This is mainly due to the dynamic epistemic logic has a stronger expression ability to deal with many problems about agent's cognitive reasoning in computer science. In this paper, we distinguish the major sorts of knowledge that the cognitive agent will hold in their interaction in the beginning, and then establish a Knowledge Acquisition System (KAS) which is added a new operator of `knowledge acquisition' to extend the basic epistemic language for bi-agent interaction. Other than that, we prove the soundness and completeness of this system, and analyze some properties about it. By this system, it is clear to explain how the agent acquires knowledge that will be transformed into common knowledge by observing the other agent's speech act. The acquired knowledge is further transformed into new private knowledge that provides the strategy options in agent's mind in interaction.
双智能体交互中知识获取的推理系统
在人工智能中,知识获取和推理是智能体获得模拟思维能力的最基本的关键要素,模拟思维能力使智能体能够进行一系列动作。目前,动态认知逻辑(Dynamic Epistemic Logic, DEL)已成为知识获取和推理建模的主要技术。这主要是由于动态认知逻辑在处理计算机科学中智能体认知推理的许多问题时具有较强的表达能力。本文首先区分了认知智能体在交互过程中所持有的主要知识种类,然后建立了一个知识获取系统(KAS),该系统增加了“知识获取”算子,扩展了双智能体交互的基本认知语言。除此之外,我们还证明了该系统的完备性,并分析了该系统的一些特性。通过这个系统,可以清楚地解释代理如何通过观察其他代理的言语行为来获取知识,这些知识将转化为共同知识。获得的知识进一步转化为新的私有知识,在交互过程中为agent的思维提供策略选择。
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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