Klyubin Alexander S, Polani Daniel, Nehaniv Chrystopher L
Adaptive Systems Research Group, School of Computer Science, University of Hertfordshire, Hatfield Herts, UK.
Neural Comput. 2007 Sep;19(9):2387-432. doi: 10.1162/neco.2007.19.9.2387.
Sensor evolution in nature aims at improving the acquisition of information from the environment and is intimately related with selection pressure toward adaptivity and robustness. Our work in the area indicates that information theory can be applied to the perception-action loop. This letter studies the perception-action loop of agents, which is modeled as a causal Bayesian network. Finite state automata are evolved as agent controllers in a simple virtual world to maximize information flow through the perception-action loop. The information flow maximization organizes the agent's behavior as well as its information processing. To gain more insight into the results, the evolved implicit representations of space and time are analyzed in an information-theoretic manner, which paves the way toward a principled and general understanding of the mechanisms guiding the evolution of sensors in nature and provides insights into the design of mechanisms for artificial sensor evolution.
自然界中的传感器进化旨在改善从环境中获取信息的能力，并且与朝向适应性和稳健性的选择压力密切相关。我们在该领域的工作表明，信息论可应用于感知 - 行动循环。本文研究了智能体的感知 - 行动循环，将其建模为因果贝叶斯网络。有限状态自动机在一个简单的虚拟世界中作为智能体控制器进行演化，以最大化通过感知 - 行动循环的信息流。信息流最大化不仅组织了智能体的行为，还组织了其信息处理。为了更深入地理解结果，我们以信息论的方式分析了演化出的空间和时间的隐式表示，这为从原理上和总体上理解指导自然界中传感器进化的机制铺平了道路，并为人工传感器进化机制的设计提供了见解。
