TL;DR
This paper introduces linear algebra tools to analyze Graph-Rewriting Automata over extended time scales, revealing organic structures and chaotic patterns that model natural complex systems.
Contribution
It presents a novel linear algebra approach for studying GRA behavior over long periods and demonstrates its effectiveness on a natural subset.
Findings
Discovery of chaotic growth patterns in GRA
Generation of organic-looking graph structures
Relevance of GRA in modeling natural systems
Abstract
Graph-Rewriting Automata (GRA) are an extension of Cellular Automata to a dynamic structure using local graph-rewriting rules. This work introduces linear algebra based tools that allow for a practical investigation of their behavior in deeply extended time scales. A natural subset of GRA is explored in different ways thereby demonstrating the benefits of this method. Some elements of the subset were discovered to create chaotic patterns of growth and others to generate organic-looking graph structures. These phenomena suggest a strong relevance of GRA in the modeling natural complex systems. The approach presented here can be easily adapted to a wide range of GRA beyond the chosen subset.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
