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Introduction
Published in Vlad P. Shmerko, Svetlana N. Yanushkevich, Sergey Edward Lyshevski, Computer Arithmetics for Nanoelectronics, 2018
Vlad P. Shmerko, Svetlana N. Yanushkevich, Sergey Edward Lyshevski
Cellular automata can be defined as a class of computing arrays of spatial topology that are characterized by local interaction and inherently parallel processing. Cellular automata are models for complex systems and processing consisting a large number of identical, simple, and locally interconnected components called cells. Cellular automata can be described in terms of dimension (1D, 2D, and 3D lattice of cells), homogeneity (all cells perform the same function), state (each cell can take one of a finite number of possible states), interconnection (each cell can interact only with neighborhoods), and discrete dynamics (each cell updates its current state according to a transition rule at discrete time). Cellular automata are a suitable model of self-assembly.
Toward Smart Cities via the Smart Grid and Intelligent Transportation Systems
Published in Hussein T. Mouftah, Melike Erol-Kantarci, Smart Grid, 2017
Stephen W. Turner, Suleyman Uludag
Accordingly, the efforts highlighted here primarily examine decentralized approaches that employ self-organizing networks of traffic elements. Approaches have typically been modeled using cellular automata or agent-based modeling (ABM) methods. Cellular automata are defined as collections of cells that exist on a regular grid in one or more dimensions. Each cell is programmed with a series of deterministic rules to modify its behavior based on the status of its nearest neighbors [103]. Early efforts recognized the ability of cellular automata to model the behavior of deterministic networks of traffic lights on very regular street structures, such as perfect two-dimensional grids of roads.
Environmental Models in DEVS
Published in Gabriel A. Wainer, Pieter J. Mosterman, Discrete-Event Modeling and Simulation, 2018
Jean-Baptiste Filippi, Teruhisa Komatsu, David R.C. Hill
Cellular Automata (CA) are widely used to model different kinds of complex systems. A cellular automaton is usually represented by a matrix field of cells executing the same program simultaneously, receiving as input the state of their neighboring cells [7,8]. CA prove to be well suited to model systems that are spatially distributed, by defining the behavior of an atomic portion of the system (a cell) in relation to a distant or direct neighborhood such as presented in Figure 14.1.
HLA: a novel hybrid model based on fixed structure and variable structure learning automata
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Saber Gholami, Ali Mohammad Saghiri, S. M. Vahidipour, M.R. Meybodi
On the one hand, learning automata are known as simple decision-making units. Recently, the theory of learning automata has been merged with different fields to create better decision-making units regarding different purposes. Some of these combinations that are described thoroughly in (Rezvanian et al., 2018) are as follows: • Cellular Learning Automata (CLAs): CLAs invest in designing a team of decision-makers that operates in a fully distributed system. They combine the computational power of cellular automata with the learning capability of learning automata in the unknown environments.• Distributed Learning Automata (DLAs): DLAs contains a team of decision-makers who try to find an appropriate path in a distributed manner, and they are organized to learn a sequence of decisions in unknown environments.• Adaptive Petrinet based on learning automata: these models focus on designing a team of learning automata which are organized based on a petrinet. These are powerful models for modelling and simulation, and their capabilities are merged with learning automata theory to present learning mechanisms that can be analysed using petrinet theories.
Preface
Published in International Journal of Parallel, Emergent and Distributed Systems, 2019
Understanding the nature of complexity exhibited by several natural processes whose dynamics depend both on space and time is still a big challenge for scientists. In this context, spatially extended systems constitute a very important class of dynamical systems for which non-trivial and unexpected collective behaviour is observed on the macroscopic scale. They are defined on a lattice and characterized by local interactions and synchronous updating. Accordingly, cellular automata seem to be the natural way to model such systems. They are spatially and temporally discrete mathematical systems characterized by local interaction and synchronous dynamical evolution. Both deterministic and probabilistic cellular automata are widely used for modelling highly non-linear/stochastic phenomena that exhibit emergent behaviours based on simple local interactions. They also may be considered as the discrete equivalent of partial differential equations that usually formed the basic models for spatio-temporal systems.
A stochastic cellular automata model of tautomer equilibria
Published in Molecular Physics, 2018
Gregory A. Bowers, Paul G. Seybold
Given the importance of tautomer and similar equilibria, the development of practical alternative models for the study of these equilibria is especially desirable. We show here that cellular automata (CA) models provide a highly efficient and fundamentally sound means for studying tautomer and similar equilibria. These models are an example of ‘agent-based’ models, which trace their origins to ideas of the mathematician/physicist J. von Neumann and the mathematician S. Ulam in the 1950s [19–21]. In contrast to the more familiar differential equation-based formulations normally employed, CA models are rule-based, typically employing rather simple rules, which nonetheless can often generate quite complex behaviours. The CA simulations take place on a one-, two- or three-dimensional grids, with time advancing in discrete time-steps (iterations). The rules of a cellular automaton can be deterministic or probabilistic (stochastic), the latter implying only a certain specified probability of an action, such as a transformation from one species to another, taking place. In an asynchronous, stochastic CA, the probabilistic rules are applied in random order to all the ingredients on the grid in each iteration. Since each run is an independent trial, the important fluctuations found in natural systems appear naturally in the output from repeated trials. The deterministic solutions emerge, as they do in the real phenomena, as averages from many trials or the results from trials with a large number of ingredients.