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.
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Roussaki, I., Papaioannou, I., Anangostou, M. (2007). Building Automated Negotiation Strategies Enhanced by MLP and GR Neural Networks for Opponent Agent Behaviour Prognosis. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_19
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DOI: https://doi.org/10.1007/978-3-540-73007-1_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
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