Learning situation-dependent costs: improving planning from probabilistic robot execution
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Online learning about other agents in a dynamic multiagent system
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Learning what is relevant to the effects of actions for a mobile robot
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Learning to coordinate actions in multi-agent systems
Readings in agents
Multi-agent reinforcement learning: independent vs. cooperative agents
Readings in agents
Multiple Comparisons in Induction Algorithms
Machine Learning
Coordinated Hospital Patient Scheduling
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Complex Goal Criteria and Its Application in Design-to-Criteria Scheduling
Complex Goal Criteria and Its Application in Design-to-Criteria Scheduling
Criteria-Directed Heuristic Task Scheduling TITLE2:
Criteria-Directed Heuristic Task Scheduling TITLE2:
Benefits of learning in negotiation
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Using self-diagnosis to adapt organizational structures
Proceedings of the fifth international conference on Autonomous agents
An Adaptive Agent Society for Environmental Scanning through the Internet
PRIMA 2001 Proceedings of the 4th Pacific Rim International Workshop on Multi-Agents, Intelligent Agents: Specification, Modeling, and Applications
Distributed agents for cost-effective monitoring of critical success factors
Decision Support Systems
Evolution of the GPGP/TÆMS Domain-Independent Coordination Framework
Autonomous Agents and Multi-Agent Systems
The Soft Real-Time Agent Control Architecture
Autonomous Agents and Multi-Agent Systems
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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 T脝EMS, 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.