Belief Manipulation Through Propositional Announcements

Belief Manipulation Through Propositional Announcements

Aaron Hunter, Fran├žois Schwarzentruber, Eric Tsang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 1109-1115. https://doi.org/10.24963/ijcai.2017/154

Public announcements cause each agent in a group to modify their beliefs to incorporate some new piece of information, while simultaneously being aware that all other agents are doing the same. Given a set of agents and a set of epistemic goals, it is natural to ask if there is a single announcement that will make each agent believe the corresponding goal. This problem is known to be undecidable in a general modal setting, where the presence of nested beliefs can lead to complex dynamics. In this paper, we consider not necessarily truthful public announcements in the setting of AGM belief revision. We prove that announcement finding in this setting is not only decidable, but that it is simpler than the corresponding problem in the most simplified modal logics. We then describe AnnB, an implemented tool that uses announcement finding as the basis for controlling robot behaviour through belief manipulation.
Keywords:
Knowledge Representation, Reasoning, and Logic: Belief Change
Knowledge Representation, Reasoning, and Logic: Reasoning about Knowlege and Belief
Knowledge Representation, Reasoning, and Logic: Logics for Knowledge Representation