摘要翻译：
最近的工作表明，信息论如何扩展传统的完全理性博弈理论，以允许有限理性的代理人。关联的数学框架可用于求解约束优化问题。这是通过将问题转化为一个迭代博弈来实现的，其中每个agent控制问题的不同变量，因此agent移动的联合概率分布给出了目标函数的期望值。智能体的动力学设计为最小化该联合分布的拉格朗日函数。在这里，我们说明了拉格朗日模型中拉格朗日参数的更新是一种自动退火的形式，它将联合分布越来越紧密地集中在优化目标函数的联合运动上。然后我们研究“半坐标”变量变换的使用。这将智能体的联合状态从优化问题的变量中分离出来，两者之间通过一个Oto映射连接起来。我们给出的实验说明了这种转换的能力，以促进优化。我们重点讨论了一类特殊的变换，其中智能体的统计独立状态导致优化变量上的混合分布。对$K$-SAT约束满足问题和$NK$函数的无约束极小化问题进行了计算机实验。
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英文标题：
《Distributed Constrained Optimization with Semicoordinate Transformations》
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作者：
William Macready and David Wolpert
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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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：
  Recent work has shown how information theory extends conventional full-rationality game theory to allow bounded rational agents. The associated mathematical framework can be used to solve constrained optimization problems. This is done by translating the problem into an iterated game, where each agent controls a different variable of the problem, so that the joint probability distribution across the agents' moves gives an expected value of the objective function. The dynamics of the agents is designed to minimize a Lagrangian function of that joint distribution. Here we illustrate how the updating of the Lagrange parameters in the Lagrangian is a form of automated annealing, which focuses the joint distribution more and more tightly about the joint moves that optimize the objective function. We then investigate the use of ``semicoordinate'' variable transformations. These separate the joint state of the agents from the variables of the optimization problem, with the two connected by an onto mapping. We present experiments illustrating the ability of such transformations to facilitate optimization. We focus on the special kind of transformation in which the statistically independent states of the agents induces a mixture distribution over the optimization variables. Computer experiment illustrate this for $k$-sat constraint satisfaction problems and for unconstrained minimization of $NK$ functions.
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PDF链接：
https://arxiv.org/pdf/0811.0823