Issue Clustering and Distributed Genetic Algorithms for Multi-issue Negotiations

Most real-world negotiation involves multiple interdependent issues, which makes an agent´s utility functions nonlinear. Traditional negotiation mechanisms, which were designed for linear utilities, do not fare well in nonlinear contexts. One of the main challenges in developing effective nonlinear negotiation protocols is scalability; they can produce excessively high failure rates, when there are many issues, due to computational intractability. One reasonable approach to reducing computational cost, while maintaining good quality outcomes, is to decompose the utility space into several largely independent sub-spaces. In this paper, we propose a new method for decomposing a utility space based on interdependency of issues and employing the genetic algorithms in each issue-group. In addition, the experimental results demonstrate that our method can find higher quality solutions than existing works.