Building Automated Negotiation Strategies Enhanced by MLP and GR Neural Networks for Opponent Agent Behaviour Prognosis
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.
Keywordsnegotiating agents MLP & GR neural networks NN-assisted negotiation strategies opponent behaviour prediction
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