Abstract
In this paper, we present first of all the working principles of an accuracy based learning classifier system. We also discuss the use of learning classifier systems for learning from data by considering a sample application. The sample application, the Terrain Reasoner Weight Adapter (TRWA), is a system that learns near optimal weights to be used by a path planner while generating routes. Manually generated weights are used to generate a sample data set for training the TRWA. We detail the TRWA and the significant improvements made to the usual XCS strategies in order to achieve our goal of using a supervised learning technique for the TRWA. A reward assignment scheme is developed. The use of tournament selection instead of roulette wheel selection for selecting two parents in the GA is also analyzed. The results obtained show the efficiency of the method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
