Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief

TL;DR

This paper introduces an efficient method for multi-agent epistemic planning that enables agents to reason about nested beliefs, transforming complex belief reasoning into classical planning problems for improved computational efficiency.

Contribution

It formalizes planning with nested beliefs and converts these problems into classical planning tasks, facilitating more efficient multi-agent belief reasoning.

Findings

Successfully formalized nested belief planning.

Demonstrated conversion to classical planning problems.

Achieved improved computational efficiency.

Abstract

Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.