Learning Quantitative Knowledge for Multiagent Coordination

David Jensen, Michael Atighetchi, Régis Vincent, and Victor Lesser, University of Massachusetts at Amherst

A central challenge of multiagent coordination is reasoning about how the actions of one agent can affect the actions of another. Knowledge of these interrelationships can help coordinate agents -- preventing conflicts and exploiting beneficial relationships among actions. We explore three interlocking methods that can be used to learn quantitative knowledge of such non-local effects in TAEMS, a well-developed framework for multiagent coordination. The surprising simplicity and effectiveness of these methods demonstrates how agents can learn domain-specific knowledge quickly, extending the utility of coordination frameworks that explicitly represent coordination knowledge.


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