摘要
Negotiation is both an important topic in multi-agent systems research and an important aspect of daily life. Many real-world negotiations are complex and involve multiple interdependent issues, therefore, there has been increasing interest in such negotiations. Existing nonlinear automated negotiation protocols have difficulty in finding solutions when the number of issues and agents is large. In automated negotiations covering multiple independent issues, it is useful to separate out the issues and reach separate agreements on each in turn. In this paper, we propose an effective approach to automated negotiations based on recursive partitioning using an issue dendrogram. A mediator first finds partial agreements in each sub-space based on bids from the agents, then combines them to produce the final agreement. When it cannot find a solution, our proposed method recursively decomposes the negotiation sub-problems using an issue dendrogram. In addition, it can improve the quality of agreements by considering previously-found partial consensuses. We also demonstrate experimentally that our protocol generates higher-optimality outcomes with greater scalability than previous methods.
Negotiation is both an important topic in multi-agent systems research and an important aspect of daily life. Many real-world negotiations are complex and involve multiple interdependent issues, therefore, there has been increasing interest in such negotiations. Existing nonlinear automated negotiation protocols have difficulty in finding solutions when the number of issues and agents is large. In automated negotiations covering multiple independent issues, it is useful to separate out the issues and reach separate agreements on each in turn. In this paper, we propose an effective approach to automated negotiations based on recursive partitioning using an issue dendrogram. A mediator first finds partial agreements in each sub-space based on bids from the agents, then combines them to produce the final agreement. When it cannot find a solution, our proposed method recursively decomposes the negotiation sub-problems using an issue dendrogram. In addition, it can improve the quality of agreements by considering previously-found partial consensuses. We also demonstrate experimentally that our protocol generates higher-optimality outcomes with greater scalability than previous methods.
