Automated Negotiation is a growing area of research in recent years as it provides a mechanism for intelligent agents representing people and institutions to coordinate their behavior in a complex environment under rational selfish assumptions. Most research in this area assumes either a single negotiation thread with a well-defined utility function for each agent involved or a set of concurrent negotiations with an ordering of outcomes in each local negotiation. In this paper, we consider an agent engaged in a set of concurrent negotiations with a utility function defined only for the complete set of agreements in all of them and no locally defined ordering of outcomes in any negotiation. The paper presents an algorithm that allows such agent to maximize its expected global utility by orchestrating its behavior in all negotiation threads. The performance of the proposed method is analyzed theoretically and empirically using simulation in the context of a trading market.

近年来，自动协商是一个不断增长的研究领域，因为它为代表人和机构的智能代理提供了一种机制，可以在理性自私的假设下在复杂的环境中协调他们的行为。该领域的大多数研究都假设单个协商线程对每个参与的代理具有明确定义的效用函数，或者在每个本地协商中具有一组并发协商和结果排序。在本文中，我们考虑从事一组并行的谈判只用了定义的效用函数的代理一套完整的协议在所有在任何谈判中都没有本地定义的结果排序。该论文提出了一种算法，允许此类代理通过在所有协商线程中协调其行为来最大化其预期的全局效用。在交易市场的背景下使用模拟从理论上和经验上分析了所提出方法的性能。