Algorithms for Optimizing Leveled Commitment Contracts
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Poaching and Distraction in Asynchronous Agent Activities
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Helping based on future expectations
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Integrative Negotiation In Complex Organizational Agent Systems
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
A survey of multi-agent organizational paradigms
The Knowledge Engineering Review
Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems
Meta-level coordination for solving negotiation chains in semi-cooperative multi-agent systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Towards a formal framework for multi-objective multiagent planning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Local negotiation in cellular networks: from theory to practice
IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
OAR: a formal framework for multi-agent negotiation
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
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In a cooperative multi-agent system that is situated in an evolving environment, agents need to dynamically adjust their negotiation attitudes towards different agents in order to achieve optimal system performance. In this paper, we construct a statistical model for a small cooperative multi-linked negotiation system. It presents the relationship between the environment, the level of local cooperation and the global system performance in a formal and clear way that allows us to explain system behavior and predict system performance. The analysis results in a set of design equations that can be used to develop distributed mechanisms that optimize the performance of the system dynamically. It helps us more concretely understand the important issue of distraction and provides us with the local attitude parameter to handle distraction effectively. This research demonstrates that sophisticated probabilistic modelling can be used to understand the behaviors of a system with complex agent interactions, and provide guidelines for the development of effective distributed control mechanisms.