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.

中文翻译：

---

开发用于连续环境的分类器系统

学习分类器系统是一种自适应系统，它通过与尝试强化学习类似的框架，通过反复试验以if-then形式定义以if-then形式定义的动作规则，来获取一组适用于多步问题的适当动作规则。由于分类器系统的输入信号被编码为二进制值，因此当处理环境状态连续变化的问题时，位串经常被加长。神经网络可以将实际值作为输入信号处理；但是，它不能应用于多步骤问题。本文提出了一种对问题做出响应的系统，该环境通过结合神经网络和分类器系统来不断改变环境状态，并从多个选项中选择动作，从而可以将输出定义为离散值。