形态学计算如何在分布式Agent中塑造集成信息
How Morphological Computation shapes Integrated Information in Embodied Agents
摘要：
集成信息理论为意识提供了一种定量的方法，并可应用于神经网络。由这样的网络控制的具体代理影响并被其环境影响。这涉及到，一方面，形态计算内的目标导向的行动，另一方面，综合信息内的控制器，代理人的大脑。在这篇文章中，我们结合不同的方法，以检查信息流之间和内部的身体，大脑和环境的代理。这使我们能够将各种信息流相互关联。我们在一个简单的实验设置测试这个框架。在那里，我们计算的最优策略为目标导向的行为的基础上的“规划作为推理”的方法，其中的信息几何EM算法被用来优化目标的可能性。形态学计算和综合信息，然后计算相对于最优策略。在不断变化的形态环境下比较这些措施的动态，突出了这两个概念之间的对立关系。涉及的形态学计算越多，大脑内部所需的信息整合就越少。为了确定大脑对智能体行为的影响，有必要额外测量进出大脑的信息流。
英文摘要：
The Integrated Information Theory provides a quantitative approach to consciousness and can be applied to neural networks. An embodied agent controlled by such a network influences and is being influenced by its environment. This involves, on the one hand, morphological computation within goal directed action and, on the other hand, integrated information within the controller, the agent's brain. In this article, we combine different methods in order to examine the information flows among and within the body, the brain and the environment of an agent. This allows us to relate various information flows to each other. We test this framework in a simple experimental setup. There, we calculate the optimal policy for goal-directed behavior based on the "planning as inference" method, in which the information-geometric em-algorithm is used to optimize the likelihood of the goal. Morphological computation and integrated information are then calculated with respect to the optimal policies. Comparing the dynamics of these measures under changing morphological circumstances highlights the antagonistic relationship between these two concepts. The more morphological computation is involved, the less information integration within the brain is required. In order to determine the influence of the brain on the behavior of the agent it is necessary to additionally measure the information flow to and from the brain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
