Scalable and Efficient Protocols by Grouping Issues in Multiple Interdependent-Issue Negotiations

Abstract

Most real-world negotiation involves multiple interdependent issues, which create agent utility functions that are nonlinear. Our research focuses on developing algorithms that enable this kind of negotiation. We present a novel bidding-based negotiation protocol that addresses the excessively high failure rates that existing approaches face when applied to highly complex nonlinear utility functions. This protocol works by using issue dependency information as follows. First, agents generate an interdependency graph by analyzing the agent's constraints. Second, a mediator identifies issue-groups based on the agents' interdependency graphs. Third, agents generate bids that are divided into these issue-groups. Finally, the mediator identifies the winning contract by finding the best combinations of bids in each issue-group. In this paper, we demonstrate that our proposed protocol is highly scalable when compared to previous efforts in a more realistic experimental setting.