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Title: State Transition in Multi-agent Epistemic Domains Using Answer Set Programming
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.  more » « less
Award ID(s):
1757207
NSF-PAR ID:
10462533
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Lecture notes in computer science
ISSN:
0302-9743
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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