Embedding arbitrary Boolean circuits into fungal automata with arbitrary update sequences
Eric Goles, Augusto Modanese, Mart\'in R\'ios-Wilson, Domingo Ruiz-Tala, Thomas Worsch

TL;DR
This paper proves that predicting the evolution of fungal automata with any update sequence containing both horizontal and vertical updates is computationally hard (P-complete), extending previous results to all such schemes.
Contribution
It establishes that the prediction problem remains P-complete for any update scheme containing both H and V, generalizing prior specific cases.
Findings
Prediction problem is P-complete for any update scheme with both H and V.
Extends previous results from specific schemes to all schemes containing H and V.
Shows computational complexity of fungal automata evolution is generally hard.
Abstract
The sandpile automata of Bak, Tang, and Wiesenfeld (Phys. Rev. Lett., 1987) are a simple model for the diffusion of particles in space. A fundamental problem related to the complexity of the model is predicting its evolution in the parallel setting. Despite decades of effort, a classification of this problem for two-dimensional sandpile automata remains outstanding. Fungal automata were recently proposed by Goles et al. (Phys. Lett. A, 2020) as a spin-off of the model in which diffusion occurs either in horizontal or vertical directions according to a so-called update scheme. Goles et al. proved that the prediction problem for this model with the update scheme is -complete. This result was subsequently improved by Modanese and Worsch (Algorithmica, 2024), who showed the problem is -complete also for the simpler updatenscheme . In this…
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.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSlime Mold and Myxomycetes Research · Cellular Automata and Applications · DNA and Biological Computing
