Representations of Space and Time in the Maximization of Information Flow in the Perception-Action Loop Export

@article{citeulike:1826819, abstract = {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.}, author = {Klyubin, Alexander S. and Polani, Daniel and Nehaniv, Chrystopher L.}, citeulike-article-id = {1826819}, citeulike-linkout-0 = {http://neco.mitpress.org/cgi/content/abstract/19/9/2387}, day = {1}, journal = {Neural Comp.}, keywords = {motor, sensory}, month = {September}, number = {9}, pages = {2387--2432}, posted-at = {2007-10-26 22:11:36}, priority = {2}, title = {Representations of Space and Time in the Maximization of Information Flow in the Perception-Action Loop}, url = {http://neco.mitpress.org/cgi/content/abstract/19/9/2387}, volume = {19}, year = {2007} }

TY - JOUR ID - citeulike:1826819 L3 - citeulike-article-id:1826819 N2 - 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. IS - 9 JF - Neural Comp. EP - 2432 TI - Representations of Space and Time in the Maximization of Information Flow in the Perception-Action Loop VL - 19 SP - 2387 KW - motor KW - sensory AU - Klyubin, Alexander AU - Polani, Daniel AU - Nehaniv, Chrystopher PY - 2007/09/01/ UR - http://neco.mitpress.org/cgi/content/abstract/19/9/2387 ER -