具有不真实公告的多智能体域规划

Loc Pham, Tran Cao Son, Enrico Pontelli
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引用次数: 0

摘要

早期用于多智能体领域的认知规划系统生成的计划包含各种类型的动作,如本体、感知或公告动作。然而,这些系统都不考虑不真实的公告,也就是说,没有一个系统可以生成包含谎言或误导性公告的计划。在本文中,我们提出了一种新的认知规划器,称为EFP3.0,用于具有不真实公告的多智能体领域。该计划器类似于EFP或EFP2.0系统,因为它是一个前向搜索计划器,可以通过采用基于Kripke的状态表示和实现基于更新模型的转换函数来处理无限嵌套的信念和常识。与EFP不同的是,EFP3.0采用了一种规范语言,该语言使用边缘条件更新模型来推理多智能体域中动作的效果。我们描述了EFP3.0的基础知识,并针对最先进的认知规划器对该系统进行了实验评估。我们讨论了可能对系统的可伸缩性和效率有用的潜在改进。
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Planning in Multi-Agent Domains with Untruthful Announcements
Earlier epistemic planning systems for multi-agent domains generate plans that contain various types of actions such as ontic, sensing, or announcement actions. However, none of these systems consider untruthful announcements, i.e., none can generate plans that contain a lying or a misleading announcement. In this paper, we present a novel epistemic planner, called EFP3.0, for multi-agent domains with untruthful announcements. The planner is similar to the systems EFP or EFP2.0 in that it is a forward-search planner and can deal with unlimited nested beliefs and common knowledge by employing a Kripke based state representation and implementing an update model based transition function. Different from EFP, EFP3.0 employs a specification language that uses edge-conditioned update models for reasoning about effects of actions in multi-agent domains. We describe the basics of EFP3.0 and conduct experimental evaluations of the system against state-of-the-art epistemic planners. We discuss potential improvements that could be useful for scalability and efficiency of the system.
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