The UMASS intelligent home project
Proceedings of the third annual conference on Autonomous Agents
Design-to-Criteria Scheduling: Real-Time Agent Control
Revised Papers from the International Workshop on Infrastructure for Multi-Agent Systems: Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems
Information sharing for distributed intrusion detection systems
Journal of Network and Computer Applications
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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.