Name
Abstract | ||
|---|---|---|
Multi-issue negotiation protocols have been studied widely and represent a promising field since most negotiation problems in the real world involve multiple issues. The vast majority of this work has assumed that negotiation issues are independent, so agents can aggregate the utilities of the issue values by simple summation, producing linear utility functions. In the real world, however, such aggregations are often unrealistic. We cannot, for example, just add up the value of car's carburetor and the value of car's engine when engineers negotiate over the design a car. These value of these choices are interdependent, resulting in nonlinear utility functions. In this paper, we address this important gap in current negotiation techniques. We propose a negotiation protocol where agents employ adjusted sampling to generate proposals, and an auction mechanism is used to find social-welfare maximizing deals. Our experimental results show that our method substantially outperforms existing methods in large nonlinear utility spaces like those found in real world contexts. Further, we show that our protocol is incentive compatible. |
Year | Venue | Field |
|---|---|---|
2006 | AAAI | Engineering management,Computer science,Artificial intelligence,Computer Science and Engineering,Machine learning,Negotiation |
DocType | Citations | PageRank |
Conference | 5 | 0.60 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takayuki Ito | 1 | 888 | 380.66 |
| Mark M. Klein | 2 | 1550 | 187.52 |
| Hiromitsu Hattori | 3 | 222 | 25.09 |