Title :
A Multi-objective Genetic Algorithm Method to Support Multi-agent Negotiations
Author :
Beheshti, R. ; Rahmani, A.T.
Author_Institution :
Dept. of Comput. Eng., Iran Univ. of Sci. & Technol., Tehran, Iran
Abstract :
Negotiations are among the most common ways that agents in a multi-agent system use to reach agreements. Because negotiations commonly are multi-lateral and multi-issue, these processes become more difficult. In the real world applications this becomes more important where the autonomous agents involved in a negotiation should reach maximum payoff in minimum time. In this work a new negotiation mechanism is proposed that is based on the multi-objective genetic algorithms. Several measures are defined that can show fitness of an offer in the set of feasible offers that an agent can have in each round of negotiations. The results show that this method can be used in real applications and is competitive with existing approaches.
Keywords :
genetic algorithms; multi-agent systems; autonomous agent; multiagent negotiation; multiobjective genetic algorithm; Autonomous agents; Conference management; Decision making; Engineering management; Genetic algorithms; Genetic engineering; Information management; Information technology; Multiagent systems; Technology management; Autonomous agent; Multi-Objective Genetic Algorithm; Negotiation;
Conference_Titel :
Future Information Technology and Management Engineering, 2009. FITME '09. Second International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-1-4244-5339-9
DOI :
10.1109/FITME.2009.154