Multi-Agent Belief Base Revision

Emiliano Lorini (1, 2) , Francois Schwarzentruber (3)

1 IRIT-LILaC - Logique, Interaction, Langue et Calcul
2 CNRS - Centre National de la Recherche Scientifique
3 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires

We present a generalization of belief base revision to the multi-agent case. In our approach agents have belief bases containing both propositional beliefs and higher-order beliefs about their own beliefs and other agents’ beliefs. Moreover, their belief bases are split in two parts: the mutable part, whose elements may change under belief revision, and the core part, whose elements do not change. We study a belief revision operator inspired by the notion of screened revision. We provide complexity results of model checking for our approach as well as an optimal model checking algorithm. Moreover, we study complexity of epistemic planning formulated in the context of our framework.

Domaines: Intelligence artificielle [cs.AI]

Emiliano Lorini, Francois Schwarzentruber. Multi-Agent Belief Base Revision. Thirtieth International Joint Conference on Artificial Intelligence (IJCAI 2021), International Joint Conferences on Artificial Intelligence Organization, Aug 2021, Montreal, Canada. pp.1959-1965, ⟨10.24963/ijcai.2021/270⟩. ⟨hal-03453911⟩