Building automated negotiation strategies enhanced by MLP and GR neural networks for opponent agent behaviour prognosis

Authors:
Ioanna Roussaki;Ioannis Papaioannou;Miltiades Anangostou
Affiliations:
National Technical University of Athens, School of Electrical and Computer Engineering, Athens, Greece;National Technical University of Athens, School of Electrical and Computer Engineering, Athens, Greece;National Technical University of Athens, School of Electrical and Computer Engineering, Athens, Greece
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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Abstract

A quite challenging research field in the artificial intelligence domain is the design and evaluation of agents handling automated negotiations on behalf of their human or corporate owners. This paper aims to enhance such agents with techniques enabling them to predict their opponents' negotiation behaviour and thus achieve more profitable results and better resource utilization. The proposed learning techniques are based on MLP and GR neural networks (NNs) that are used mainly to detect at an early stage the cases where agreements are not achievable, supporting the decision of the agents to withdraw or not from the specific negotiation thread. The designed NN-assisted negotiation strategies have been evaluated via extensive experiments and are proven to be very useful.