Abstract

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.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.