Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Sequential behavior and learning in evolved dynamical neural networks
Adaptive Behavior
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
On the computational power of neural nets
Journal of Computer and System Sciences
Neural Computation
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Reinforcement learning with hierarchies of machines
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Multi-time models for temporally abstract planning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Neural Networks - Special issue on organisation of computation in brain-like systems
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Multiple model-based reinforcement learning
Neural Computation
Imitation in animals and artifacts
Levels of dynamics and adaptive behavior in evolutionary neural controllers
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
SOS++: finding smart behaviors using learning and evolution
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Temporally adaptive networks: analysis of GasNet robot control networks
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Evolving reinforcement learning-like abilities for robots
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
2006 Special issue: Goals and means in action observation: A computational approach
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
Anticipatory Behavior in Adaptive Learning Systems
Computational virtuality in biological systems
Theoretical Computer Science
Hierarchical Classifiers for Complex Spatio-temporal Concepts
Transactions on Rough Sets IX
A gesture-based concept for speech movement control in articulatory speech synthesis
COST 2102'07 Proceedings of the 2007 COST action 2102 international conference on Verbal and nonverbal communication behaviours
Dynamical movement primitives: Learning attractor models for motor behaviors
Neural Computation
Hi-index | 0.00 |
This study describes how complex goal-directed behavior can be obtained through adaptation processes in a hierarchically organized recurrent neural network using a genetic algorithm (GA). Our experiments, using a simulated Khepera robot, showed that different types of dynamic structures self-organize in the lower and higher levels of the network for the purpose of achieving complex navigation tasks. The parametric bifurcation structures that appear in the lower level explain the mechanism of how behavior primitives are switched in a top-down way. In the higher level, a topologically ordered mapping of initial cell activation states to motor primitive sequences self-organizes by utilizing the initial sensitivity characteristics of non-linear dynamical systems. The biological plausibility of the model's essential principles is discussed.