Where Common Knowledge Cannot Be Formed, Common Belief Can -- Planning with Multi-Agent Belief Using Group Justified Perspectives

TL;DR

This paper introduces the Group Justified Perspective (GJP) model, extending epistemic planning to handle group and common beliefs efficiently, overcoming nested belief complexity in multi-agent systems.

Contribution

The paper presents the GJP model, an extension of the JP model, enabling effective planning with group and common beliefs in multi-agent epistemic scenarios.

Findings

GJP handles planning problems infeasible for existing tools.

Experimental results demonstrate GJP's efficiency and expressiveness.

GJP effectively models distributed and common beliefs in multi-agent settings.

Abstract

Epistemic planning is the sub-field of AI planning that focuses on changing knowledge and belief. It is important in both multi-agent domains where agents need to have knowledge/belief regarding the environment, but also the beliefs of other agents, including nested beliefs. When modeling knowledge in multi-agent settings, many models face an exponential growth challenge in terms of nested depth. A contemporary method, known as Planning with Perspectives (PWP), addresses these challenges through the use of perspectives and set operations for knowledge. The JP model defines that an agent's belief is justified if and only if the agent has seen evidence that this belief was true in the past and has not seen evidence to suggest that this has changed. The current paper extends the JP model to handle \emph{group belief}, including distributed belief and common belief. We call this the Group Justified Perspective (GJP) model. Using experimental problems crafted by adapting well-known benchmarks to a group setting, we show the efficiency and expressiveness of our GJP model at handling planning problems that cannot be handled by other epistemic planning tools.