Parallel Accelerated Virtual Physarum Lab Based on Cellular Automata Agents

Abstract

Self-aware and self-expressive physical systems are inspiring new methodologies for engineering solutions of complex computing problems. Among many other examples, the slime mold Physarum Polycephalum exhibits self-awareness and self-expressiveness while adapting to changes in its dynamical environment and solving resource-consuming problems like shortest path, proximity graphs or optimization of transport networks. As such, the modeling of the slime mold's behavior is essential when designing bio-inspired algorithms and hardware prototypes. The goal of this paper is to combine one of the powerful parallel computational tools, cellular automata (CA) with the adaptive potential of Physarum slime mold. Namely, we propose a CA model and multi-agent approach to imitate the behavior of the plasmodium. We then test the efficacy of the proposed model on graph problems such as the maze problem or the traveling salesman problem (TSP). Finally, the virtual Physarum model is evaluated on a data set for pattern recognition purposes and achieves to form very effectively the letters of the alphabet, especially when compared with real experiments performed to prove the efficacy of the proposed model. Furthermore, to exploit the CA's inherent parallelism and make the model's responses faster, both GPU and hardware implementations are proposed and compared. As a result, an accelerated virtual lab is developed which uses a multi-agent CA model to describe the behavior of plasmodium and can be used as an intelligent, autonomous, self-adaptive system in various heterogeneous and unknown environments spanning from different types of graph problems up to real life-time applications.

Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks

“…In this work, we build on the simulation model of Jones (2010). While other computationally efficient VPM models have been proposed-for instance, those based on cellular automata (Dourvas et al, 2019;Kalogeiton et al, 1986) and formal logic rules (Schumann & Pancerz, 2016)we adopt this agent-based model due to its ability to construct precise trajectories. Jones's model is related to agent-based modeling of crowds and flocks (Olfati-Saber, 2006;Reynolds, 1987), but uses a continuous representation of the agents' density for navigation (Jones, 2010(Jones, , 2015b.…”

Polyphorm: Structural Analysis of Cosmological Datasets via Interactive Physarum Polycephalum Visualization

“…However, there are a range of works that utilize models of Physarum polycephalum to make sense of complex systems. Efficient virtual Physarum machines have been developed that utilize cellular automata [23,40] and formal logic [64], and an approach introduced by Jones [35] uses a large number of agents to generate emergent structures. This latter approach is similar in some ways to agent-based models that model crowds and flocks of birds [49,59], but differs in that it uses a continuous representation of the agents density for navigation [35,36].…”

A New Structure of 2-State Number-Conserving Cellular Automata

“…The state of each cell is a finite integer, which changes according to a rule that depends on the cell states in the neighborhood. There are many variations of CAs, depending on the dimension, state, and neighborhood, and they can also be applied to a wide variety of fields [4], [5], [14].…”