Comparing the Performance of MLP and RBF Neural Networks Employed by Negotiating Intelligent Agents

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 strategies have been compared and empirically evaluated via numerous experiments.