The Cyber Rodent Project: Exploration of Adaptive Mechanisms for Self-Preservation and Self-Reproduction

By Kenji Doya, Eiji Uchibe. 2005

64 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “, in The Cyber Rodent Project (Doya and Uchibe, 2005) that investigated the evolution of learning by seeking to implement a number of features such as (1) evolution of neural controllers, (2) learning of foraging and mating behaviors, (3) evolution of learning architectures and metaparameters, (4) s”
as a Robot Controller Neuromodulation as a Robot Controller A Brain-Inspired Strategy for Controlling Autonomous Robots (2009, background): “In a robotic system that has correlates with features of the noradrenergic system, cyber rodents explored new behaviors when their battery packs were full but took more exploitative behavior when their battery packs were nearly empty [23].”
The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World (2008, background): “Robots controlled by reinforcement learning (Sutton & Barto, 1998) adapt their behavior through a reward prediction error (Doya & Uchibe, 2005; Guenter, Hersch, Calinon, & Billard, 2007; Iida, Kuwayama, Kanoh, Kato, & Itoh, 2004; Kondo & Ito, 2004; Nakamura, Mori, Sato, & Ishii, 2007; Stone,…”
Constrained reinforcement learning from intrinsic and extrinsic rewards (2007, methodology): “We develop the experimental setup that integrates the CPGRL and the technique of the embodied evolution in our multi-robot platform named " Cyber Rodents " (Doya & Uchibe, 2005).”
Neuromodulation as a robot controller (2009, background): “In a robotic system that has correlates with features of the noradrenergic system, cyber rodents explored new behaviors when their battery packs were full but took more exploitative behavior when their battery packs were nearly empty [23].”
Evolving neuromodulatory topologies for reinforcement learning-like problems (2007, methodology): “Computational models of modulatory dynamics in neural systems have been proposed with the aim of understanding the neural substrate underlying reinforcement learning-like behaviour [4], [13], [14], [15], [16], [17], [18], [ 19 ].”
Deep Learning Techniques for Music Generation - A Survey (2017, methodology): “Reproduced from [35] with the permission of SAGE Publications, Inc.”
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012, background): “, 2010), making neuromodulation particularly suitable for modeling and implementing learning processes (Sporns and Alexander, 2002; Doya, 2002; Doya and Uchibe, 2005; Soula et al., 2005; Farries and Fairhall, 2007; Krichmar, 2008; Cox and Krichmar, 2009).”
Free-Energy Based Reinforcement Learning for Vision-Based Navigation with High-Dimensional Sensory Inputs (2010, methodology): “The Cyber Rodent robot (see [ 9 ] for details) used in the experiments is a small mobile robot.”