We discuss some issues concerning the application of learning classifier systems to real-valued applications. In particular, we focus on the possibility of classifying data by crisp and fuzzy intervals, showing the effect of their granularity on the learning performance. We introduce the concept of sensorial cluster and we discuss the difference between cluster aliasing and perceptual aliasing. We show the impact of different choices on the performance of both crisp and fuzzy learning classifier systems applied to a mobile, autonomous, robotic agent.

Fuzzy and Crisp representation of real-valued input for learning classifier systems

BONARINI, ANDREA;BONACINA, COSTANTE;MATTEUCCI, MATTEO
2000

Abstract

We discuss some issues concerning the application of learning classifier systems to real-valued applications. In particular, we focus on the possibility of classifying data by crisp and fuzzy intervals, showing the effect of their granularity on the learning performance. We introduce the concept of sensorial cluster and we discuss the difference between cluster aliasing and perceptual aliasing. We show the impact of different choices on the performance of both crisp and fuzzy learning classifier systems applied to a mobile, autonomous, robotic agent.
Learning Classifier System: From Foundation to Application
9783540677291
INF
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/506660
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