Name
Abstract | ||
|---|---|---|
This paper introduces a negotiation strategy based on uncompromising degree. Agents get information of the negotiation opponents in each iteration by means of Bayesian learning mechanism, and then bring forward the proposals for the next iteration according to the negotiation strategy based on uncompromising degree. In this paper, we analyze Bayesian learning mechanism and use it to get the opponents' information; introduce the negotiation strategy based on uncompromising degree; discuss how the remaining time affects negotiation strategy. Our strategy regards the whole negotiation process as a dynamic interaction process, which enhances the usage of MAS in complex and dynamic environment. The experiments show that our strategy has good negotiation performance. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/IAT.2004.8 | IAT |
Keywords | Field | DocType |
next iteration,whole negotiation process,dynamic interaction process,negotiation strategy,negotiation opponent,uncompromising degree,remaining time,dynamic environment,good negotiation performance,learning artificial intelligence,bayesian learning,multi agent systems | Bayesian inference,Computer science,Multi-agent system,Artificial intelligence,Negotiation theory,Negotiation | Conference |
ISBN | Citations | PageRank |
0-7695-2101-0 | 0 | 0.34 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo An | 1 | 892 | 106.05 |
| Lianggui Tang | 2 | 1 | 2.04 |
| Shuangqing Li | 3 | 26 | 3.27 |
| Daijie Cheng | 4 | 9 | 3.22 |