A market architecture for multi-agent contracting
AGENTS '98 Proceedings of the second international conference on Autonomous agents
An agent-based approach for building complex software systems
Communications of the ACM
JELIA '00 Proceedings of the European Workshop on Logics in Artificial Intelligence
Expectation-Oriented Analysis and Design
AOSE '01 Revised Papers and Invited Contributions from the Second International Workshop on Agent-Oriented Software Engineering II
A survey of multi-agent organizational paradigms
The Knowledge Engineering Review
Designing a Simulation Middleware for FIPA Multiagent Systems
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Journal of Artificial Intelligence Research
Agent-based computing: promise and perils
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
The contract net: a formalism for the control of distributed problem solving
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
Engineering Applications of Artificial Intelligence
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Agent coordination is a fundamental task in designing and operating multiagent systems. However, in dynamically changing environments, coordination must balance proactive and reactive behaviors in order to enable efficient operations while retaining the necessary flexibility to react to unforeseen events. This paper introduces adaptive agent relationships for coping with these contradictory requirements. In this approach, agents dynamically establish relationships which are represented as interaction patterns. On the one hand, these patterns enable efficient coordination by restricting the number of potential interaction flows to those offering the best estimated outcome. On the other hand, they can adapt to environmental changes, as the agents continuously reconsider their relationships in a feedback loop of estimated interaction flows and actually observed coordination outcomes. The paper formalizes the agent decision-making process enabling adaptive relationships and applies it to a logistics network scenario. A comparative evaluation demonstrates its ability to efficiently coordinate agent interaction in a dynamic environment.