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Generating Boolean Functions on Totalistic Automata Networks
arXiv - CS - Formal Languages and Automata Theory Pub Date : 2021-07-30 , DOI: arxiv-2107.14799
Eric GolesUnconventional Computing Laboratory, University of the West of England, Bristol, UK and Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile, Andrew AdamatzkyUnconventional Computing Laboratory, University of the West of England, Bristol, UK, Pedro MontealegreFacultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile, Martín Ríos-WilsonDepartamento de Ingeniería Matemática, FCFM, Universidad de Chile, Santiago, Chile and Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France

We consider the problem of studying the simulation capabilities of the dynamics of arbitrary networks of finite states machines. In these models, each node of the network takes two states 0 (passive) and 1 (active). The states of the nodes are updated in parallel following a local totalistic rule, i.e., depending only on the sum of active states. Four families of totalistic rules are considered: linear or matrix defined rules (a node takes state 1 if each of its neighbours is in state 1), threshold rules (a node takes state 1 if the sum of its neighbours exceed a threshold), isolated rules (a node takes state 1 if the sum of its neighbours equals to some single number) and interval rule (a node takes state 1 if the sum of its neighbours belong to some discrete interval). We focus in studying the simulation capabilities of the dynamics of each of the latter classes. In particular, we show that totalistic automata networks governed by matrix defined rules can only implement constant functions and other matrix defined functions. In addition, we show that t by threshold rules can generate any monotone Boolean functions. Finally, we show that networks driven by isolated and the interval rules exhibit a very rich spectrum of boolean functions as they can, in fact, implement any arbitrary Boolean functions. We complement this results by studying experimentally the set of different Boolean functions generated by totalistic rules on random graphs.

中文翻译:

在完全自动机网络上生成布尔函数

我们考虑研究有限状态机任意网络的动力学模拟能力的问题。在这些模型中,网络的每个节点都采用两种状态 0(被动)和 1(主动)。节点的状态按照局部总体规则并行更新,即仅取决于活动状态的总和。考虑了四类总体规则:线性或矩阵定义的规则(如果节点的每个邻居都处于状态 1,则节点采用状态 1)、阈值规则(如果其邻居的总和超过阈值,则节点采用状态 1)、孤立的规则(如果其邻居的总和等于某个单个数字,则节点采用状态 1)和间隔规则(如果其邻居的总和属于某个离散间隔,则节点采用状态 1)。我们专注于研究后一类的动力学模拟能力。特别是,我们表明由矩阵定义规则控制的极权自动机网络只能实现常数函数和其他矩阵定义函数。此外,我们证明 t by threshold 规则可以生成任何单调布尔函数。最后,我们展示了由隔离和区间规则驱动的网络展示了非常丰富的布尔函数范围,因为它们实际上可以实现任何任意布尔函数。我们通过实验研究随机图上的极权规则生成的一组不同的布尔函数来补充这一结果。我们表明由矩阵定义规则控制的极权自动机网络只能实现常数函数和其他矩阵定义函数。此外,我们证明 t by threshold 规则可以生成任何单调布尔函数。最后,我们展示了由隔离和区间规则驱动的网络展示了非常丰富的布尔函数范围,因为它们实际上可以实现任何任意布尔函数。我们通过实验研究随机图上的极权规则生成的一组不同的布尔函数来补充这一结果。我们表明由矩阵定义规则控制的极权自动机网络只能实现常数函数和其他矩阵定义函数。此外,我们证明 t by threshold 规则可以生成任何单调布尔函数。最后,我们展示了由隔离和区间规则驱动的网络展示了非常丰富的布尔函数范围,因为它们实际上可以实现任何任意布尔函数。我们通过实验研究随机图上的极权规则生成的一组不同的布尔函数来补充这一结果。
更新日期:2021-08-02
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