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This paper proposes a novel framework enabling AI systems to detect and model other agents from raw observations, a critical capability for facilitating real-world cooperative behavior. By leveraging reinforcement learning priors, the approach addresses an underexplored area distinct from standard inverse reinforcement learning, offering new pathways for multi-agent interaction research.
Artificial Empathy: Towards a Framework for Unsupervised Agency Detection and Policy Reconstruction
arXiv:2608.23030v1 Announce Type: new Abstract: We study how an AI system can identify and model other agents in its environment from observation alone, which is a capability necessary for cooperative behaviour in the real world. This problem is less constrained than inverse reinforcement learning and remains largely unexplored. We propose a framework that uses a reinforcement learning agent, trained on an independent task as a prior about agentic dynamics, to perform agency detection and policy reconstruction.
Abstract:We study how an AI system can identify and model other agents in its environment from observation alone, which is a capability necessary for cooperative behaviour in the real world. This problem is less constrained than inverse reinforcement learning and remains largely unexplored. We propose a framework that uses a reinforcement learning agent, trained on an independent task as a prior about agentic dynamics, to perform agency detection and policy reconstruction.
Artificial Intelligence (cs.AI)
arXiv:2608.23030 [cs.AI]
(or arXiv:2608.23030v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.23030 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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