一种新的多目标聚类策略选择方法 博弈论算法

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摘要翻译：
影响博弈理论算法效率的最重要因素是时间和博弈复杂度。在本研究中，我们提供了一个优雅的方法来处理高复杂度的博弈论多目标聚类方法在大规模数据集。在这里，我们开发了一种方法，它从每个玩家的策略配置文件中选择策略的子集。在这种情况下，支付矩阵的大小显著减小，这对时间复杂度有显著影响。因此，具有较多数据的实际问题可以以较少的计算复杂度进行处理。虽然策略集可能会随着数据点的增加而增加，但所提出的策略选择模型大大缩小了策略空间，在每个局部博弈中，簇被细分为若干个子簇。结果表明，该方法能有效地降低问题的计算复杂度。
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英文标题：
《A Novel Strategy Selection Method for Multi-Objective Clustering
  Algorithms Using Game Theory》
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作者：
Mahsa Badami, Ali Hamzeh, Sattar Hashemi
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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：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：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：
  The most important factors which contribute to the efficiency of game-theoretical algorithms are time and game complexity. In this study, we have offered an elegant method to deal with high complexity of game theoretic multi-objective clustering methods in large-sized data sets. Here, we have developed a method which selects a subset of strategies from strategies profile for each player. In this case, the size of payoff matrices reduces significantly which has a remarkable impact on time complexity. Therefore, practical problems with more data are tractable with less computational complexity. Although strategies set may grow with increasing the number of data points, the presented model of strategy selection reduces the strategy space, considerably, where clusters are subdivided into several sub-clusters in each local game. The remarkable results demonstrate the efficiency of the presented approach in reducing computational complexity of the problem of concern.
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PDF链接：
https://arxiv.org/pdf/1208.3432

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关键词：博弈论 多目标 Presentation Applications Intelligence each 博弈 聚类 模型 缩小