[计算机科学] 中定性期望效用估计的有效算法 可能性案例推理 [推广有奖]

摘要翻译：
提出了一种基于可能性的定性期望效用估计的有效算法。它对于每个可能的决策被分配一个多属性可能性分布的决策机制是有用的。传统的基于离散化的期望效用计算方法的计算复杂度随着属性数的增加呈指数增长，当属性数较多时可能变得不可行。我们给出了一系列定理和引理来证明我们的算法的正确性，该算法避免了线性计算复杂度。该算法已应用于多方多属性谈判中最有希望的合作伙伴的选择，也可用于谈判过程中潜在报价的决策。
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英文标题：
《Efficient algorithm for estimation of qualitative expected utility in
  possibilistic case-based reasoning》
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作者：
Jakub Brzostowski, Ryszard Kowalczyk
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最新提交年份：
2012
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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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英文摘要：
  We propose an efficient algorithm for estimation of possibility based qualitative expected utility. It is useful for decision making mechanisms where each possible decision is assigned a multi-attribute possibility distribution. The computational complexity of ordinary methods calculating the expected utility based on discretization is growing exponentially with the number of attributes, and may become infeasible with a high number of these attributes. We present series of theorems and lemmas proving the correctness of our algorithm that exibits a linear computational complexity. Our algorithm has been applied in the context of selecting the most prospective partners in multi-party multi-attribute negotiation, and can also be used in making decisions about potential offers during the negotiation as other similar problems.
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PDF链接：
https://arxiv.org/pdf/1207.1377