Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Integrated systems based on behaviors
ACM SIGART Bulletin
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Understanding intelligence
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Intelligence Without Reason
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Coordinating Multiple Agents via Reinforcement Learning
Autonomous Agents and Multi-Agent Systems
An Architecture for Behavior-Based Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Hierarchical multi-agent reinforcement learning
Autonomous Agents and Multi-Agent Systems
Empirical Studies in Action Selection with Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Fast learning in networks of locally-tuned processing units
Neural Computation
Comparison of RBF Network Learning and Reinforcement Learning on the Maze Exploration Problem
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Multi-robot path planning using co-evolutionary genetic programming
Expert Systems with Applications: An International Journal
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A comparison of behavior-based and planning approaches of robot control is presented in this paper. We focus on miniature mobile robotic agents with limited sensory abilities. Two reactive control mechanisms for an agent are considered-a radial basis function neural network trained by evolutionary algorithm and a traditional reinforcement learning algorithm over a finite agent state space. The control architecture based on localization and planning is compared to the former method.