Learning Quantitative Knowledge for Multiagent Coordination

D. Jensen, M. Atighetchi, R. Vincent and V. Lesser (1999). Learning quantitative knowledge for multiagent coordination. Proceedings of The Sixteenth National Conference on Artificial Intelligence (AAAI-99). pp. 24-31.

Abstract

A central challenge of multiagent coordination is reasoning about how the 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. We explore three interlocking methods that 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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