Genetic algorithms approach to a negotiation support system

It is argued that negotiation rules can be learned and invented by means of genetic algorithms. The work presented introduces a method, a system design, and a prototype implementation that uses genetic-based machine learning to acquire negotiation rules. The learned rules support a party involved in a two-party bargaining problem with multiple issues. It is assumed that both parties work towards a compromise deal. The method provides a framework in which genetic-based learning is applied repetitively on a changing problem representation. System design proposes a problem representation that is adequate to express bargaining processes and that is at the same time conducive to genetic-based learning. The authors report results of experiments with the prototype implementation. These results indicate that genetically learned rules, when used in real negotiations, yield results that are better than results obtained by humans in the same negotiation. The experiments indicate considerable robustness of genetically learned rules with respect to varying parameters defining the genetic operations on which the system relies in modeling negotiations. In terms of user support, experimental results show that in the bargaining process, a good rule is one that advises conceding in small steps and bringing new issues into the negotiation process