University of Hertfordshire

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Original languageEnglish
Pages (from-to)2387-2432
JournalNeural Computation
Volume19
Issue9
DOIs
Publication statusPublished - 2007

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.

Notes

Original article can be found at: http://www.mitpressjournals.org/ Copyright MIT Press. DOI: 10.1162/neco.2007.19.9.2387 [Full text of this article is not available in the UHRA]

ID: 102384