Abstract
In this paper we develop a state transition function for partially observable multi-agent epistemic domains and implement it using Answer Set Programming (ASP). The transition function computes the next state upon an occurrence of a single action. Thus it can be used as a module in epistemic planners. Our transition function incorporates ontic, sensing and announcement actions and allows for arbitrary nested belief formulae and general common knowledge. A novel feature of our model is that upon an action occurrence, an observing agent corrects his (possibly wrong) initial beliefs about action precondition and his observability. By examples, we show that this step is necessary for robust state transition. We establish some properties of our state transition function regarding its soundness in updating beliefs of agents consistent with their observability.
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Notes
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Details of state transition in these examples can be found in our online appendix at https://github.com/yizmirlioglu/Epistemic.
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Acknowledgment
The authors have been partially supported by NSF grants 2151254, 1914635 and 1757207. Tran Cao Son was also partially supported by NSF grant 1812628.
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Izmirlioglu, Y., Pham, L., Son, T.C., Pontelli, E. (2022). State Transition in Multi-agent Epistemic Domains Using Answer Set Programming. In: Gottlob, G., Inclezan, D., Maratea, M. (eds) Logic Programming and Nonmonotonic Reasoning. LPNMR 2022. Lecture Notes in Computer Science(), vol 13416. Springer, Cham. https://doi.org/10.1007/978-3-031-15707-3_21
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