The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Learning-based automated negotiation between shipper and forwarder
Computers and Industrial Engineering
Predicting opponent's moves in electronic negotiations using neural networks
Expert Systems with Applications: An International Journal
Neural networks against genetic algorithms for negotiating agent behaviour prediction
Web Intelligence and Agent Systems
Expert Systems with Applications: An International Journal
Multi-agent Negotiation Model Based on RBF Neural Network Learning Mechanism
IITAW '08 Proceedings of the 2008 International Symposium on Intelligent Information Technology Application Workshops
Improving multi-agent negotiations using multi-objective PSO algorithm
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Learning other agents' preferences in multiagent negotiation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
Any rational agent involving in a multi-agent systems negotiation tries to optimize the negotiation outcome based on its interests or utility function. Negotiations in multi-agent systems are usually complex, and a lot of variables exist which affect the agents' decisions. This becomes more visible in competitive or multi-issue types of negotiations. So, the negotiator agents need an efficient mechanism to do well. The key solution to this type of problems is employing a powerful and operative learning method. An agent tries to learn information it obtains from its environment in order to make the best decisions during the negotiations. In real-world multi-agent negotiations, the main source of usable data is the negotiators' behaviors. So, a good learning approach should be able to extract the buried information in the 'negotiation history'. In this work, we used an ARTMAP artificial neural network as a powerful and efficient learning tool. The main role of this component is to predict other agents' actions/offers in the next rounds of negotiation. When an agent finds out what are the most possible offers which will be proposed, it can predict the outcomes of its decisions. In addition, a new method to apply this information and determine next moves in a negotiation is proposed. The obtained experimental results show that this method can be used effectively in real multi-agent negotiations.