执行不确定性下基于信任的鲁棒高效任务分配机制
Trust-Based Mechanisms for Robust and Efficient Task Allocation in the Presence of Execution Uncertainty
摘要：
Vickrey-Clarke-Groves（VCG）机制通常用于将任务分配给自私和理性的代理。VCG机制是激励兼容的直接机制，是有效的（即，最大化社会效用）和个人理性（即，代理人更愿意加入而不是选择退出）。然而，这些机制的一个重要假设是，代理将“总是”成功地完成其分配的任务。显然，这种假设在许多现实世界的应用中是不现实的，在这些应用中，代理人可能而且经常会失败。此外，代理是否被认为已经失败可能被不同的代理不同地感知。这种关于代理在给定任务中成功的概率的主观感知通常被捕获并推理为使用“信任”的概念。在这种背景下，在本文中，我们研究了新的机制，考虑到代理之间的信任分配任务时的设计。具体来说，我们开发了一类新的机制，称为“基于信任的机制”，它可以考虑到多个主观的措施，成功的代理在一个给定的任务的概率，并产生分配，最大限度地提高社会效用，同时确保没有代理获得负效用。然后，我们表明，这样的机制提出了一个具有挑战性的新的组合优化问题（即NP完全），设计了一种新的表示来解决这个问题，并开发了一个有效的整数规划解决方案（可以在40秒内解决约2x10^5个可能的分配）。
英文摘要：
Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive compatible, direct mechanisms that are efficient (i.e., maximise social utility) and individually rational (i.e., agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will "always" successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications, where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agents probability of succeeding at a given task are often captured and reasoned about using the notion of "trust". Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called "trust-based mechanisms", that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^5 possible allocations in 40 seconds).
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