de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

Evolution of the sensorimotor control in an autonomous agent

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83985

Huber,  S
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84072

Mallot,  HA
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Huber, S., Mallot, H., & Bülthoff, H. (1996). Evolution of the sensorimotor control in an autonomous agent. From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, 449-457.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-EB4E-1
Zusammenfassung
Visually guided agents are introduced, that evolve their sensor orientations and sensorimotor coupling in a simulated evolution. The work builds on neurobiological results from various aspects of insect navigation and the architecture of the ``Vehicles‘‘ of Braitenberg (1984). Flies have specialized visuomotor programs for tasks like compensating for deviations from the course, tracking, and landing, which involve the analysis of visual motion information. We use genetic algorithms to evolve the obstacle avoidance behavior. The sensor orientations and the transmission weights between sensor input and motor output evolve with the sensors and motors acting in a closed loop of perception and action. The influence of the crossover and mutation probabilities on the outcome of the simulations, specifically the maximum fitness and the convergence of the population are tested.