Improving multi-agent negotiations using multi-objective PSO algorithm

Authors:
Ahmad Esmaeili;Nasser Mozayani
Affiliations:
Department of Computer Engineering, Iran University of Science & Technology, Tehran, Iran;Department of Computer Engineering, Iran University of Science & Technology, Tehran, Iran
Venue:
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Year:
2010

Citing 7
Cited 1

Multiagent compromise via negotiation

Distributed Artificial Intelligence (Vol. 2)
Negotiation and cooperation in multi-agent environments

Artificial Intelligence - Special issue on economic principles of multi-agent systems
Multi-attribute Utility Theoretic Negotiation for Electronic Commerce

Agent-Mediated Electronic Commerce III, Current Issues in Agent-Based Electronic Commerce Systems (includes revised papers from AMEC 2000 Workshop)
On Agent-Mediated Electronic Commerce

IEEE Transactions on Knowledge and Data Engineering
A MOPSO algorithm based exclusively on pareto dominance concepts

EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Agent behaviors in virtual negotiation environments

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A weighted sum genetic algorithm to support multiple-partymultiple-objective negotiations

IEEE Transactions on Evolutionary Computation

A new mechanism for negotiations in multi-agent systems based on ARTMAP artificial neural network

KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Negotiation over limited resources, as a way for the agents to reach agreement, is one of the significant topics in Multi-Agent Systems (MASs). Most of the models proposed for negotiation suffer from different limitations in the number of the negotiation parties and issues as well as some constraining assumptions such as availability of unlimited computational resources and complete information about the participants. In this paper we make an attempt to ease the limitations specified above by means of a distributive agent based mechanism underpinned by Multi-Objective Swarm Optimization (MOPSO), as a fast and effective learning technique to handle the complexity and dynamics of the real-world negotiations. The experimental results of the proposed method reveal its effectiveness and high performance in presence of limited computational resources and tough deadlines.