哲学指导下的元胞自动机和人工神经网络自动编程
Automatic Programming of Cellular Automata and Artificial Neural Networks Guided by Philosophy
摘要：
许多计算机模型如元胞自动机和人工神经网络已被开发并成功应用。然而，在某些情况下，这些模型可能会限制可能的解决方案，或者其解决方案可能难以解释。为了克服这个问题，我们概述了一种新的方法，所谓的allagmatic方法，自动编程和执行模型，尽可能少的限制，同时保持人类的可解释性。前面我们根据结构（空间维度）和操作（时间维度）的哲学概念描述了元模型及其构建块。它们是实体、环境和更新函数，它们一起抽象地描述了细胞自动机、人工神经网络以及可能的任何类型的计算机模型。通过在演化计算中自动组合这些构建块，与元模型的关系可能会增加可解释性，并且模型可能会通过元模型转换为更可解释的模型。我们提出了通用和面向对象的编程实现的实体和他们的环境作为动态和通用数组和更新功能作为一种方法。我们展示了两个实验，其中一个简单的细胞自动机和一个人工神经网络自动编程，编译和执行。分别在元胞自动机和人工神经网络中成功地进化和学习目标状态。我们的结论是，allagmatic方法可以创建和执行元胞自动机和人工神经网络模型在一个自动化的方式与哲学的指导。
英文摘要：
Many computer models such as cellular automata and artificial neural networks have been developed and successfully applied. However, in some cases, these models might be restrictive on the possible solutions or their solutions might be difficult to interpret. To overcome this problem, we outline a new approach, the so-called allagmatic method, that automatically programs and executes models with as little limitations as possible while maintaining human interpretability. Earlier we described a metamodel and its building blocks according to the philosophical concepts of structure (spatial dimension) and operation (temporal dimension). They are entity, milieu, and update function that together abstractly describe cellular automata, artificial neural networks, and possibly any kind of computer model. By automatically combining these building blocks in an evolutionary computation, interpretability might be increased by the relationship to the metamodel, and models might be translated into more interpretable models via the metamodel. We propose generic and object-oriented programming to implement the entities and their milieus as dynamic and generic arrays and the update function as a method. We show two experiments where a simple cellular automaton and an artificial neural network are automatically programmed, compiled, and executed. A target state is successfully evolved and learned in the cellular automaton and artificial neural network, respectively. We conclude that the allagmatic method can create and execute cellular automaton and artificial neural network models in an automated manner with the guidance of philosophy.
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