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Development of a classifier system for a continuous environment
Author(s) -
Hayashida Tomohiro,
Nishizaki Ichiro,
Sekizaki Shinya,
Ogasawara Yuki
Publication year - 2019
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.12209
Subject(s) - classifier (uml) , computer science , artificial neural network , artificial intelligence , binary number , reinforcement learning , learning classifier system , machine learning , pattern recognition (psychology) , mathematics , arithmetic
A learning classifier system is an adaptive system that obtains a set of appropriate action rules that adapts to multistep problems by training action rules defined in if‐then form by trial and error process, in a similar framework as reinforcement learning. Because of that the input signals of the classifier system are encoded into binary values, bit strings are often lengthened when dealing with such a problem that the state of the environment continuously changes. A neural network can treat with real values as input signal; however, it cannot be applied to multistep problems. This paper proposes a system that responds to problems such that the state of the environment continuously changes by combining a neural network and a classifier system, and actions are selected from multiple options, so that output can be defined as discrete values. In order to verify the effectiveness of the proposed system, this paper conducts several numerical experiments using benchmarks corresponding to multistep problems defined by continuous values.