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Approximate and online multi-issue negotiation.

机译:近似和在线多议题协商。

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摘要

This paper analyzes bilateral multi-issue negotiation between self-interested autonomous agents. The agents have time constraints in the form of both deadlines and discount factors. There are m > 1 issues for negotiation where each issue is viewed as a pie of size one. The issues are indivisible (i.e., individual issues cannot be split between the parties; each issue must be allocated in its entirety to either agent). Here different agents value different issues differently. Thus, the problem is for the agents to decide how to allocate the issues between themselves so as to maximize their individual utilities. For such negotiations, we first obtain the equilibrium strategies for the case where the issues for negotiation are known a priori to the parties. Then, we analyse their time complexity and show that finding the equilibrium offers is an NP-hard problem, even in a complete information setting. In order to overcome this computational complexity, we then present negotiation strategies that are approximately optimal but computationally efficient, and show that they form an equilibrium. We also analyze the relative error (i.e., the difference between the true optimum and the approximate). The time complexity of the approximate equilibrium strategies is O(nm/2) where n is the negotiation deadline and the relative error. Finally, we extend the analysis to online negotiation where different issues become available at different time points and the agents are uncertain about their valuations for these issues. Specifically, we show that an approximate equilibrium exists for online negotiation and show that the expected difference between the optimum and the approximate is O(√m). These approximate strategies also have polynomial time complexity. © 2007 IFAAMAS.
机译:本文分析了利己自治代理人之间的双边多问题谈判。代理商具有截止日期和折扣因子两种形式的时间限制。有m> 1个要协商的问题,其中每个问题都被视为一个大小的馅饼。这些问题是不可分割的(即,各个问题不能在各方之间分配;每个问题必须全部分配给任何一个代理商)。在这里,不同的代理人对不同问题的重视程度不同。因此,问题是代理商决定如何在他们之间分配问题,以最大化他们的个人效用。对于这样的谈判,我们首先针对各方事先知道谈判问题的情况,获得均衡策略。然后,我们分析了它们的时间复杂度,并表明,即使在完整的信息环境中,找到均衡报价也是NP难题。为了克服这种计算复杂性,我们然后提出了近似最优但计算效率高的协商策略,并表明它们形成了平衡。我们还分析了相对误差(即真实的最佳值和近似值之间的差)。近似均衡策略的时间复杂度为O(nm / 2),其中n是协商期限和相对误差。最后,我们将分析扩展到在线协商,在这种情况下,不同的问题会在不同的时间点出现,并且代理商不确定这些问题的估值。具体来说,我们表明存在在线协商的近似平衡,并且表明最优与近似之间的期望差为O(√m)。这些近似策略也具有多项式时间复杂度。 ©2007 IFAAMAS。

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