Learning Quantitative Knowledge for Multiagent Coordination

A central challenge of multiagent coordination is reasoning about howthe actions of one agent affect the actions of another. Knowledge of these interrelationships can help coordinate agents -- preventing conflicts and exploiting beneficial relationships among actions. Weexplore three interlocking methods that learn quantitative knowledge of such non-local effects in T/EMS, a well-developed frameworkfor multiagent coordination. Thesurprising simplicity and effectiveness of these methods demonstrates howagents can learn domain-specificknowledge quickly, extendingthe utility of coordination frameworks that explicitly represent coordination knowledge.

Authors

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

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