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ADDRESSING UTILITY SPACE COMPLEXITY IN NEGOTIATIONS INVOLVING HIGHLY UNCORRELATED, CONSTRAINT-BASED UTILITY SPACES

机译:涉及高度不相关,基于约束的实用程序空间的谈判中的实用程序空间复杂度

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There is an increasing interest in complex automated negotiations, where agents negotiate about multiple, interdependent issues and agent utility functions exhibit low autocorrelation. In these scenarios, the negotiation mechanisms used to find agreement solutions among agents tend to fail due to the complexity of agents' preference spaces, and this tendency increases as the degree of autocorrelation decreases. In this paper, we propose an automated negotiation model specially tailored for highly uncorrelated utility spaces based on weighted constraints. The model relies on a mediated, auction-based interaction protocol and a set of heuristic mechanisms for bidding and deal identification. To address the challenges raised by highly uncorrelated utility spaces, we propose to use a quality factor, which allows agents to balance utility and deal probability when placing their bids or when searching for agreement regions among these bids. Experiments show that the proposed negotiation model achieves high optimality results and low failure rates even in negotiation scenarios involving highly uncorrelated utility spaces, thus outperforming previous approaches.
机译:人们对复杂的自动协商越来越感兴趣,在这种情况下,座席就多个相互依存的问题进行谈判,而座席效用函数显示出较低的自相关性。在这些情况下,由于代理偏好空间的复杂性,用于在代理之间查找协议解决方案的协商机制往往会失败,并且这种趋势会随着自相关程度的降低而增加。在本文中,我们提出了一种基于加权约束为高度不相关的效用空间量身定制的自动协商模型。该模型依赖于中介的基于拍卖的交互协议以及一组用于招标和交易识别的启发式机制。为了解决高度不相关的效用空间带来的挑战,我们建议使用质量因数,该因数可让代理商在下标或在这些投标中搜索协议区域时平衡效用和交易概率。实验表明,所提出的协商模型即使在涉及高度不相关的效用空间的协商场景下,也能达到较高的最优结果和较低的失效率,从而优于以前的方法。

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