Distributed Manufacturing Scheduling Using Intelligent Agents
IEEE Intelligent Systems
Optimal Negotiation Strategies for Agents with Incomplete Information
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Negotiation in multi-agent systems
The Knowledge Engineering Review
Multiagent approach for the representation of information in a decision support system
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
Supporting a multicriterion decision making and multi-agent negotiation in manufacturing systems
Intelligent Decision Technologies
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Agents and multi-agent systems constitute nowadays a very active field of research. This field is very multidisciplinary since it is sustained by Artificial Intelligence, Distributed Systems, Software Engineering, etc. In most agent applications, the autonomous components need to interact. They need to communicate in order to solve differences of opinion and conflicts of interest. They also need to work together or simply inform each other. It is however important to note that a lot of existing works do not take into account the agents' preferences. In addition, individual decisions in the multi-agent domain are rarely sufficient for producing optimal plans which satisfy all the goals. Therefore, agents need to cooperate to generate the best multi-agent plan through sharing tentative solutions, exchanging sub goals, or having other agents' goals to satisfy. In this paper, we propose a new negotiation mechanism independent of the domain properties in order to handle real-time goals. The mechanism is based on the well-known Contract net Protocol. Integrated Station of Production agents will be equipped with a sufficient behavior to carry out practical operations and simultaneously react to the complex problems caused by the dynamic scheduling in real situations. These agents express their preferences by using ELECTRE III method in order to solve differences. The approach is tested through simple scenarios.