arXiv:2605.02249v2 Announce Type: replace Abstract: We investigate the belief revision problem in epistemic planning, i.e., what will be the beliefs of all agents in a multi-agent system after an agent gains the belief in some state property. Based on the standard representation in epistemic planning of agents' beliefs via a single multi-agent Kripke model, we generalize the classical AGM belief revision postulates to the multi-agent setting, with the aim to provide a formal framework for evaluating dynamic epistemic reasoning frameworks in which the beliefs of all agents as the result of acti
This paper extends fundamental theoretical work on belief revision, which is crucial for the development and robustness of advanced AI systems, particularly in multi-agent environments, as AI agents become more prevalent.
A formal framework for multi-agent belief revision is essential for building more reliable, interpretable, and adaptable AI agents, impacting their deployment in complex real-world scenarios.
This research provides a theoretical foundation, generalizing classical belief revision postulates to multi-agent systems, which can lead to more sophisticated and predictable AI agent behavior.
- · AI researchers
- · Developers of multi-agent AI systems
- · Autonomous systems sector
- · AI systems lacking robust belief revision
- · Frameworks based on ad-hoc belief update mechanisms
Improved theoretical understanding of how AI agents update their beliefs in collaborative or competitive environments.
Development of more robust and auditable AI agents capable of complex decision-making under uncertainty.
Enhanced AI agent interoperability and reliability in critical applications, leading to wider adoption in enterprise and defense.
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