Rules of encounter: designing conventions for automated negotiation among computers
Rules of encounter: designing conventions for automated negotiation among computers
Foundations of distributed artificial intelligence
Neural network design
Software agents
Strategic negotiation in multiagent environments
Strategic negotiation in multiagent environments
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Cases on Worldwide E-Commerce: Theory in Action
Cases on Worldwide E-Commerce: Theory in Action
Understanding Neural Networks; Computer Explorations
Understanding Neural Networks; Computer Explorations
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Automating negotiation for m-services
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Neural Networks
Detecting Unsuccessful Automated Negotiation Threads When Opponents Employ Hybrid Strategies
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
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One of the means that improve the performance and sophistication of systems in the e-business domain is mobile intelligent agents' technology. In this framework, a quite challenging research field is the design and evaluation of agents handling automated negotiations on behalf of their human or corporate owners. This paper proposes to enhance such agents with learning techniques, in order to achieve more profitable results for the parties they represent. The proposed learning techniques are based on MLP or RBF neural networks (NNs) and are quite lightweight. They aim to reduce the cases of unsuccessful negotiations and maximize the client's utility. The designed NN-assisted negotiation strategies1 have been compared and empirically evaluated via numerous experiments.