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Planning Over Multi-Agent Epistemic States: A Classical Planning Approach

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Abstract

Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.

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... This is to avoid mistakes, such as the agent obtaining a false address or disregarding the needed address. Shvo et al.'s (2020) approach was tested using three different epistemic planners: Muise et al.'s (2015) RP-MEP, Huang et al.'s (2017) MEPK, and Le et al.'s (2018) EFP. As input into these planners, they specify their problem scenario using a multi-agent modal logic called KD45 n . ...
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... Moreover, a formalisation such as ours lends itself to various types of implementations. For example, the synthesis of (epistemic) programs and plans (Wang and Zhang 2005;Baral et al. 2017;Muise et al. 2015;McIlraith and Son 2002) that achieve goals in socio-technical applications in a fair manner is an worthwhile research agenda. Likewise, enforcing fairness constraints while factoring for the relationships between individuals in social networks (Farnadi et al. 2018), or otherwise contextualising attributes against other concepts in a relational knowledge base (Aziz et al. 2018;Fu et al. 2020) are also worthwhile. ...
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... This is to avoid mistakes, such as the agent obtaining a false address or disregarding the needed address. Shvo et al.'s (2020) approach was tested using three different epistemic planners: Muise et al.'s (2015) RP-MEP, Huang et al.'s (2017) MEPK, and Le et al.'s (2018) EFP. As input into these planners, they specify their problem scenario using a multi-agent modal logic called 45 % . ...
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