摘要翻译：
我们考虑了多Agent系统，其中Agent的偏好是通过顺序多数投票聚合的：每个决策是通过执行一系列成对比较来做出的，其中每个比较是Agent之间的加权多数投票。在许多现实生活中，由于隐私问题或正在进行的启发过程，代理偏好的不完全性是常见的。此外，关于如何汇总偏好可能存在不确定性。例如，议程（一棵树叶上标有正在比较的决策的树）可能还不知道或不确定。因此，我们研究当偏好可能不完全，当议程可能不确定时，如何确定集体最优决策（也称为赢家）。我们表明，无论议程如何，在计算上很容易确定一个候选决策是否总是赢，或者可能赢。另一方面，在计算上很难知道在至少一个议程中，对于至少一个代理偏好的完成，一个候选决策是否获胜。即使必须平衡议程，使每个候选人的决定面临相同数量的多数票，这些结果也仍然存在。这些结果对于偏好激发的推理是有用的。它们有助于理解任务的复杂性，例如确定是否可以集体做出决定，以及知道是否可以通过适当地安排议程来操纵胜利者。
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英文标题：
《Dealing with incomplete agents' preferences and an uncertain agenda in
  group decision making via sequential majority voting》
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作者：
Maria Pini, Francesca Rossi, Brent Venable and Toby Walsh
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Computer Science        计算机科学
二级分类：Multiagent Systems        多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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英文摘要：
  We consider multi-agent systems where agents' preferences are aggregated via sequential majority voting: each decision is taken by performing a sequence of pairwise comparisons where each comparison is a weighted majority vote among the agents. Incompleteness in the agents' preferences is common in many real-life settings due to privacy issues or an ongoing elicitation process. In addition, there may be uncertainty about how the preferences are aggregated. For example, the agenda (a tree whose leaves are labelled with the decisions being compared) may not yet be known or fixed. We therefore study how to determine collectively optimal decisions (also called winners) when preferences may be incomplete, and when the agenda may be uncertain. We show that it is computationally easy to determine if a candidate decision always wins, or may win, whatever the agenda. On the other hand, it is computationally hard to know wheth er a candidate decision wins in at least one agenda for at least one completion of the agents' preferences. These results hold even if the agenda must be balanced so that each candidate decision faces the same number of majority votes. Such results are useful for reasoning about preference elicitation. They help understand the complexity of tasks such as determining if a decision can be taken collectively, as well as knowing if the winner can be manipulated by appropriately ordering the agenda.
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PDF链接：
https://arxiv.org/pdf/0909.4441