摘要翻译：
解释适应性行为是人工智能研究的中心问题。在这里，我们将自适应代理形式化为输入和输出（I/O）序列上的混合分布。混合物的每一个分布都构成一个“可能世界”，但代理人并不知道它实际面对的是哪一个可能世界。问题是以一种与真实世界兼容的方式来调整I/O流。真实世界的I/O分布与不确定可能世界的agent期望的I/O分布之间的Kullback-Leibler(KL)散度可以得到适应的自然度量。在纯输入流的情况下，贝叶斯混合提供了一个众所周知的解决方案。然而，我们表明，在I/O流的情况下，这种解决方案失败了，因为输出是由代理本身发出的，并且需要干预演算提供的不同的概率语法。在此基础上，我们得到了一个贝叶斯控制规则，该规则允许在I/O流上对混合分布的自适应行为进行建模。该规则可能允许一种基于最小KL原理的自适应控制的新方法。
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英文标题：
《A Bayesian Rule for Adaptive Control based on Causal Interventions》
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作者：
Pedro A. Ortega, Daniel A. Braun
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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：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  Explaining adaptive behavior is a central problem in artificial intelligence research. Here we formalize adaptive agents as mixture distributions over sequences of inputs and outputs (I/O). Each distribution of the mixture constitutes a `possible world', but the agent does not know which of the possible worlds it is actually facing. The problem is to adapt the I/O stream in a way that is compatible with the true world. A natural measure of adaptation can be obtained by the Kullback-Leibler (KL) divergence between the I/O distribution of the true world and the I/O distribution expected by the agent that is uncertain about possible worlds. In the case of pure input streams, the Bayesian mixture provides a well-known solution for this problem. We show, however, that in the case of I/O streams this solution breaks down, because outputs are issued by the agent itself and require a different probabilistic syntax as provided by intervention calculus. Based on this calculus, we obtain a Bayesian control rule that allows modeling adaptive behavior with mixture distributions over I/O streams. This rule might allow for a novel approach to adaptive control based on a minimum KL-principle.
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PDF链接：
https://arxiv.org/pdf/0911.5104