Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Market-based control: a paradigm for distributed resource allocation
Market-based control: a paradigm for distributed resource allocation
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Coordination of Distributed Problem Solvers
Coordination of Distributed Problem Solvers
Commitments in the Architecture of a Limited, Rational Agent
PRICAI '96 Proceedings from the Workshop on Intelligent Agent Systems, Theoretical and Practical Issues
The Agentis Agent Interaction Model
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
A-Teams: An Agent Architecture for Optimization and Decision Support
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
Increasing Resource Utilization and Task Performance by Agent Cloning
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
Adaptive load balancing: a study in multi-agent learning
Journal of Artificial Intelligence Research
A Classification Schema to Volumes 1 to 5 of the Intelligent Agents Series
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
Improving self-organized resource allocation with effective communication
AP2PC'08 Proceedings of the 7th international conference on Agents and Peer-to-Peer Computing
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Coordination is a recurring theme in multiagent systems design. We consider the problem of achieving coordination in a system where the agents make autonomous decisions based solely on local knowledge. An open theoretical issue is what goes into achieving effective coordination? There is some folklore about the importance of the knowledge held by the different agents, but the rest of the rich agent landscape has not been explored in depth. The present paper seeks to delineate the different components of an abstract architecture for agents that influence the effectiveness of coordination. Specifically, it proposes that the extent of the choices available to the agents as well as the extent of the knowledge shared by them are both important for understanding coordination in general. These lead to a richer view of coordination that supports a more intuitive set of claims. This paper supports its conceptual conclusions with experimental results based on simulation.