Research on Method of Multi-agent Negotiation Strategy Selection

Aiming at adopting what kind of strategy to argue in Argument-based negotiation of agents, this paper adopts methods of Case-Based Reasoning (CBR) and reinforcement learning together and put forward a model for negotiation strategy selection based on Multi-Agent System (MAS) via constructing case base about negotiation strategy and searching in the existing case base according to the similarity of the attributes that are proposed in advance, in order to find the most similar case, evaluate the strength of argument by reference the reusable case, and select the negotiation strategy. If there is no reusable case, reinforcement learning method will be used to evaluate and select the argument to send, and update the case base. Relevant examples and prototype system is realized for further analysis and testing. This method not only has the high efficiency of case-based reasoning but also takes on the dynamic accommodation of reinforcement learning, and perfects the mechanism of negotiation strategy selection.