Expansive automata networks

Bridoux, Florian; Gadouleau, Maximilien; Theyssier, Guillaume

Abstract

An Automata Network is a map where Q is a finite alphabet. It can be viewed as a network of n entities, each holding a state from Q, and evolving according to a deterministic synchronous update rule in such a way that each entity only depends on its neighbors in the network's graph, called interaction graph. In this work we introduce the following property called expansivity: the observation of the sequence of states at any given node is sufficient to determine the initial configuration of the whole network. A major trend in automata network theory is to understand how the interaction graph affects dynamical properties of f. Our first result is a characterization of interaction graphs that allow expansivity. Moreover, we show that this property is generic among linear automata networks over such graphs with large enough alphabet. We show however that the situation is more complex when the alphabet is fixed independently of the size of the interaction graph: no alphabet is sufficient to obtain expansivity on all admissible graphs, and only non-linear solutions exist in some cases. Besides, we show striking differences between the linear and the general non-linear case, in particular we prove that deciding expansivity is PSPACE-complete in the general case, while it can be done in polynomial time in the linear case. Finally, we consider a stronger version of expansivity where we ask to determine the initial configuration from any large enough observation of the system. We show that it can be achieved for any number of nodes and naturally gives rise to maximum distance separable codes.

Citation

Bridoux, F., Gadouleau, M., & Theyssier, G. (2020). Expansive automata networks. Theoretical Computer Science, 843, 25-44. https://doi.org/10.1016/j.tcs.2020.06.019

• Journal Article Type: Article
• Acceptance Date: Jun 12, 2020
• Online Publication Date: Jun 18, 2020
• Publication Date: Dec 2, 2020
• Deposit Date: Aug 5, 2020
• Publicly Available Date: Jun 18, 2021
• Journal: Theoretical Computer Science
• Print ISSN: 0304-3975
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 843
• Pages: 25-44
• DOI: https://doi.org/10.1016/j.tcs.2020.06.019
• Public URL: https://durham-repository.worktribe.com/output/1258914