A Negotiation Model Based on Bayesian Learning
An B · 2005
In Multi-Agent systems where each Agent has a different goal, Agent must be able to solve conflicts aris- ing in the process of achieving its goal, with incomplete knowledge about other Agents. Negotiation is an effective ap- proach to solve these problems. This paper introduces a negotiation model based on Bayesian learning, called NMBL. Agent gets information of the negotiation opponents in every iteration by means of Bayesian learning, updates the pri- or knowledge of the negotiation opponents and then brings forward the offer of the next iteration according to negotia- tion strategies based on the conflicting point and un-compromising degree. NMBL regards the whole negotiation pro- cess as a dynamic interaction conduct, which reveals the dynamic characteristic of Multi-Agent systems' NMBL also has a relatively strong learning ability. The experiments show that this model has good negotiation performance.