MAAISep 17, 2021

Comprehensive Multi-Agent Epistemic Planning

arXiv:2109.08301v11 citations
Originality Incremental advance
AI Analysis

This work addresses the lack of comprehensive tools for analyzing information flows in multi-agent systems, which is crucial for applications in fields such as security and politics, though it appears incremental relative to existing MEP approaches.

The paper tackles the Multi-agent Epistemic Planning (MEP) problem by formalizing an environment for comprehensive characterization of agents' knowledge/beliefs interactions, resulting in a new action-based language and an implemented epistemic planner designed for flexibility across domains like economy and security.

Over the last few years, the concept of Artificial Intelligence has become central in different tasks concerning both our daily life and several working scenarios. Among these tasks automated planning has always been central in the AI research community. In particular, this manuscript is focused on a specialized kind of planning known as Multi-agent Epistemic Planning (MEP). Epistemic Planning (EP) refers to an automated planning setting where the agent reasons in the space of knowledge/beliefs states and tries to find a plan to reach a desirable state from a starting one. Its general form, the MEP problem, involves multiple agents who need to reason about both the state of the world and the information flows between agents. To tackle the MEP problem several tools have been developed and, while the diversity of approaches has led to a deeper understanding of the problem space, each proposed tool lacks some abilities and does not allow for a comprehensive investigation of the information flows. That is why, the objective of our work is to formalize an environment where a complete characterization of the agents' knowledge/beliefs interaction and update is possible. In particular, we aim to achieve such goal by defining a new action-based language for multi-agent epistemic planning and to implement an epistemic planner based on it. This solver should provide a tool flexible enough to reason on different domains, e.g., economy, security, justice and politics, where considering others' knowledge/beliefs could lead to winning strategies.

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