A Case Based Agent Multi-Issue Negotiation Model

Multi-agents multi-issue negotiation under incomplete information is a challenge in open environment. However, until now, the strategy of optimal counter-offer generating under incomplete information is not ideal. Previous work usually use indirect approaches to acquire the preferences of opponents through a variety of data mining of other methods such as the researches of Fatima. On the other hand, agents usually have some experiences and domain knowledge which may help them get better negotiation results. This fact inspires the authors to directly investigate negotiation using case-based method. For this purpose, the authors propose an agent multi-issue negotiation model under incomplete information based on cases and game theory. The Cases are regarded as successful interactions and can be reused in future according to the similarity. A Pareto optimal result is proved in this paper. In particular, the optimal counter-offer can ensure the maximal utility of oneself and the maximal similarity of offer for opponents. The computational complexity of the proposed algorithm is polynomial order and it is commonly lower than that of Fatima as long as the scale of cases base is limited to a bounded quantities. Experimental results indicate that the utility and reaching time of the experiments have an advantage over that of human beings and the method of Lin et al. It improves the work of Fatima.