Paper Info
Title
Autonomous Acquisition of Fuzzy Rules for Mobile Robot Control: First Results from two Evolutionary Computation Approaches
Abstract
Abstract We describe two architectures that autonomously acquire fuzzy control rules to provide ,reactive behavioural competencies ,in a simulated mobile robotics application. One ,architecture is a “Pittsburgh”-style Fuzzy ,Classifier System (Pitt1). The other architecture is a “Michigan”- style Fuzzy Classifier System ,(Mich1). We tested the architectures on their ability to acquire an“investigative” obstacle ,avoidance competency. We found,that Mich1 implemented amore,local incremental search than the other architecture. In simpler environments,Mich1 was typically able to find ,adequate ,solutions with significantly fewer ,fitness evaluations. Since fitness evaluation can be very time consuming,in this application, it could be a strong positive factor. However, when the rule set must implement,a competency ,in more ,complex environments, the situation is somewhat different. The superior ability of Pitt1 to retain a number,of schema ,in the population during the process of optimisation, is then a crucial strength. 1,BACKGROUND
Year
Venue
Keywords
2000
GECCO
mobile robot,evolutionary computing,obstacle avoidance,fuzzy control
Field
DocType
Citations 
Obstacle avoidance,Population,Neuro-fuzzy,Computer science,Fuzzy set operations,Fuzzy logic,Evolutionary computation,Artificial intelligence,Fuzzy control system,Machine learning,Robotics
Conference
4
PageRank 
References 
Authors
0.45
8
2
Name
Order
Citations
PageRank
Anthony G. Pipe125539.08
Brian Carse225926.31