Paper Info
Title
A robot behavior-learning experiment using Particle Swarm Optimization for training a neural-based animat
Abstract
We investigate the use of particle swarm optimization (PSO), and compare with genetic algorithms (GA), for a particular robot behavior-learning task: the training of an animat behavior totally determined by a fully-recurrent neural network, and with which we try to fulfill a simple exploration and food foraging task. The target behavior is simple, but the learning task is challenging because of the dynamic complexity of fully-recurrent neural networks. We show that standard PSO yield very good results for this learning problem, and appears to be much more effective than simple GA.
Year
DOI
Venue
2008
10.1109/ICARCV.2008.4795790
ICARCV
Keywords
Field
DocType
learning (artificial intelligence),particle swarm optimisation,recurrent neural nets,robots,animat behavior,exploration task,food foraging,genetic algorithms,neural-based animat,particle swarm optimization,recurrent neural network,robot behavior-learning experiment,animat,behavior-learning,genetic algorithms,particle swarm optimization,recurrent neural network
Particle swarm optimization,Recurrent neural nets,Computer science,Recurrent neural network,Animat,Artificial intelligence,Behavior-based robotics,Robot,Artificial neural network,Machine learning,Genetic algorithm
Conference
ISBN
Citations 
PageRank 
978-1-4244-2287-6
0
0.34
References 
Authors
9
1
Name
Order
Citations
PageRank
Fabien Moutarde15415.26