Research on adaptive optimization strategy in intelligent argumentation-based negotiation

In argumentation-based negotiation based on multi-agent, if a negotiator agent is endowed with ability of self-learning, then it can acquire much more information about opponent´s costs and benefits to achieve the purpose of improving negotiated efficiency. This paper discusses the problem of adaptive strategy in intelligent argumentation-based negotiation, presents a generating process of adaptive strategy, optimizes and improves the process by using a method of machine learning to help negotiator to determine valid candidate concessional attributes and concessional values. Finally, this paper also describes an implementing process of the strategy model and explains it in details. The research results of this paper provide new ideas and measures for solving the problem that how to generate reasonable adaptive strategies in argumentation-based negotiation.