Results for 'cellular automata'

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  1. Cellular Automata.Francesco Berto & Jacopo Tagliabue - 2012 - Stanford Encyclopedia of Philosophy.
    Cellular automata (henceforth: CA) are discrete, abstract computational systems that have proved useful both as general models of complexity and as more specific representations of non-linear dynamics in a variety of scientific fields. Firstly, CA are (typically) spatially and temporally discrete: they are composed of a finite or denumerable set of homogeneous, simple units, the atoms or cells. At each time unit, the cells instantiate one of a finite set of states. They evolve in parallel at discrete time (...)
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  2.  20
    Classifying Cellular Automata Automatically: Finding Gliders, Filtering, and Relating Space-Time Patterns, Attractor Basins, and theZ Parameter.Andrew Wuensche - 1999 - Complexity 4 (3):47-66.
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  3.  23
    Reversible Cellular Automata with Memory of Delay Type.Ramón Alonso-Sanz - 2014 - Complexity 20 (1):49-56.
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  4. Cellular Automata, Modeling, and Computation.Anouk Barberousse, Sara Franceschelli & Cyrille Imbert - unknown
    Cellular Automata (CA) based simulations are widely used in a great variety of domains, fromstatistical physics to social science. They allow for spectacular displays and numerical predictions. Are they forall that a revolutionary modeling tool, allowing for “direct simulation”, or for the simulation of “the phenomenon itself”? Or are they merely models "of a phenomenological nature rather than of a fundamental one”? How do they compareto other modeling techniques? In order to answer these questions, we present a systematic (...)
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  5.  38
    Cellular Automata and the Sciences of Complexity.Howard Gutowitz - 1996 - Complexity 1 (6):29-35.
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  6.  49
    How to Make Dull Cellular Automata Complex by Adding Memory: Rule 126 Case Study.Genaro J. Martínez, Andrew Adamatzky, Juan C. Seck‐Tuoh‐Mora & Ramon Alonso‐Sanz - 2010 - Complexity 15 (6):34-49.
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  7.  15
    Cellular Automata and the Sciences of Complexity (Part I): A Review of Some Outstanding Problems in the Theory of Cellular Automata[REVIEW]Howard Gutowitz - 1996 - Complexity 1 (5):16-22.
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  8.  48
    Combining Causal Bayes Nets and Cellular Automata: A Hybrid Modelling Approach to Mechanisms.Alexander Gebharter & Daniel Koch - 2018 - British Journal for the Philosophy of Science:000-000.
    Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser (2016) pointed out—they have problems with capturing relevant spatial and structural information. In this paper we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes (...)
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  9.  15
    Simulating Intraurban Land Use Dynamics Under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian.Jun Yang, Weiling Liu, Yonghua Li, Xueming Li & Quansheng Ge - 2018 - Complexity 2018:1-17.
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  10.  69
    Cognitive Cellular Automata.Pete Mandik - 2008 - In Complex Biological Systems:. Icfai University Press.
    In this paper I explore the question of how artificial life might be used to get a handle on philosophical issues concerning the mind-body problem. I focus on questions concerning what the physical precursors were to the earliest evolved versions of intelligent life. I discuss how cellular automata might constitute an experimental platform for the exploration of such issues, since cellular automata offer a unified framework for the modeling of physical, biological, and psychological processes. I discuss (...)
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  11.  13
    A Cellular Automata Model Can Quickly Approximate UDP and TCP Network Traffic.Richard R. Brooks, Christopher Griffin & T. Alan Payne - 2004 - Complexity 9 (3):32-40.
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  12. Fine Tuning Explained? Multiverses and Cellular Automata.Francisco José Soler Gil & Manuel Alfonseca - 2013 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 44 (1):153-172.
    The objective of this paper is analyzing to which extent the multiverse hypothesis provides a real explanation of the peculiarities of the laws and constants in our universe. First we argue in favor of the thesis that all multiverses except Tegmark’s “mathematical multiverse” are too small to explain the fine tuning, so that they merely shift the problem up one level. But the “mathematical multiverse" is surely too large. To prove this assessment, we have performed a number of experiments with (...)
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  13.  10
    How to Make Dull Cellular Automata Complex by Adding Memory: Rule 126 Case Study.Genaro J. Martínez, Andrew Adamatzky, Juan C. Seck-Tuoh-Mora & Ramon Alonso-Sanz - 2010 - Complexity:NA-NA.
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  14.  16
    Cellular Automata (Abstract and Discussion): Complex Nonadaptive Systems.Erica Jen - forthcoming - Complexity.
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  15.  19
    Asynchronous Cellular Automata and Pattern Classification.Biswanath Sethi, Souvik Roy & Sukanta Das - 2016 - Complexity 21 (S1):370-386.
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  16.  15
    Weyl, Dirac and Maxwell Quantum Cellular Automata: Analitical Solutions and Phenomenological Predictions of the Quantum Cellular Automata Theory of Free Fields.Alessandro Bisio, Giacomo Mauro D’Ariano, Paolo Perinotti & Alessandro Tosini - 2015 - Foundations of Physics 45 (10):1203-1221.
    Recent advances on quantum foundations achieved the derivation of free quantum field theory from general principles, without referring to mechanical notions and relativistic invariance. From the aforementioned principles a quantum cellular automata theory follows, whose relativistic limit of small wave-vector provides the free dynamics of quantum field theory. The QCA theory can be regarded as an extended quantum field theory that describes in a unified way all scales ranging from an hypothetical discrete Planck scale up to the usual (...)
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  17.  16
    Basins of Attraction in Cellular Automata.Andrew Wuensche - 2000 - Complexity 5 (6):19-25.
  18. Cellular Automata in Fluid Dynamics: Not so Different.Anouk Barberousse & Cyrille Imbert - forthcoming - Studies in History and Philosophy of Modern Physics.
  19.  9
    Distributed Control of a Manufacturing System with One-Dimensional Cellular Automata.Irving Barragan-Vite, Juan C. Seck-Tuoh-Mora, Norberto Hernandez-Romero, Joselito Medina-Marin & Eva S. Hernandez-Gress - 2018 - Complexity 2018:1-15.
    We present a distributed control modeling approach for an automated manufacturing system based on the dynamics of one-dimensional cellular automata. This is inspired by the fact that both cellular automata and manufacturing systems are discrete dynamical systems where local interactions given among their elements can lead to complex dynamics, despite the simple rules governing such interactions. The cellular automaton model developed in this study focuses on two states of the resources of a manufacturing system, namely, (...)
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  20.  18
    The Emergence of Dynamical Complexity: An Exploration Using Elementary Cellular Automata.Eduardo Mizraji - 2004 - Complexity 9 (6):33-42.
  21.  31
    Guidelines for Dynamics‐Based Parameterization of One‐Dimensional Cellular Automata Rule Spaces.Gina Maira Barbosa de Oliveira, Pedro P. B. de Oliveira & Nizam Omar - 2000 - Complexity 6 (2):63-71.
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  22.  2
    An Information-Based Classification of Elementary Cellular Automata.Enrico Borriello & Sara Imari Walker - 2017 - Complexity 2017:1-8.
    We propose a novel, information-based classification of elementary cellular automata. The classification scheme proposed circumvents the problems associated with isolating whether complexity is in fact intrinsic to a dynamical rule, or if it arises merely as a product of a complex initial state. Transfer entropy variations processed by cellular automata split the 256 elementary rules into three information classes, based on sensitivity to initial conditions. These classes form a hierarchy such that coarse-graining transitions observed among elementary (...)
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  23.  14
    Binomial Representation of Cryptographic Binary Sequences and Its Relation to Cellular Automata.Sara D. Cardell & Amparo Fúster-Sabater - 2019 - Complexity 2019:1-13.
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  24.  4
    Reversible Transitions in a Cellular Automata-Based Traffic Model with Driver Memory.Tomoko Sakiyama & Ikuo Arizono - 2019 - Complexity 2019:1-8.
    Here, we develop a new cellular automata-based traffic model. In this model, individual vehicles cannot estimate global traffic flows but can only detect the vehicle ahead. Each vehicle occasionally adjusts its velocity based on the distance to the vehicle in front. Our model generates reversible phase transitions in the vehicle flux over a wide range of vehicle densities, and the traffic system undergoes scale-free evolution with respect to the flux. We thus believe that our model reveals the relationship (...)
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  25.  26
    Is Prediction Possible? Chaotic Behavior of Multiple Equilibria Regulation Model in Cellular Automata Topology.Ioannis D. Katerelos & Andreas G. Koulouris - 2004 - Complexity 10 (1):23-36.
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  26.  30
    Emergent Behavior in Two Complex Cellular Automata Rule Sets.Christopher J. Hazard, Kyle R. Kimport & David H. Johnson - 2005 - Complexity 10 (5):45-55.
  27.  24
    Combinatorial Properties of Some Cellular Automata Related to the Mosaic Cycle Concept.Claude Lobry & Hervé Elmoznino - 2000 - Acta Biotheoretica 48 (3-4):219-242.
    A cellular automaton that is related to the "mosaic cycle concept" is considered. We explain why such automata sustain very often, but not always, n-periodic trajectories (n being the number of states of the automaton). Our work is a first step in the direction of a theory of these type of automata which might be useful in modeling mosaic successions.
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  28.  17
    Control Parameters in Boolean Networks and Cellular Automata Revisited From a Logical and a Sociological Point of View.Jürgen Klüver & Jörn Schmidt - 1999 - Complexity 5 (1):45-52.
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  29.  26
    Evolutionary Design of One-Dimensional Rule Changing Cellular Automata Using Genetic Algorithms.遺伝的アルゴリズムによるルール変化型一次元セルオートマトンの進化.Wu Yun & Hitoshi Kanoh - 2003 - Transactions of the Japanese Society for Artificial Intelligence 18:325-332.
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  30.  18
    The Global Dynamics of Cellular Automata, by Andrew Wuensche and Mike Lesser.Stuart Kauffman - 2000 - Complexity 5 (6):47-48.
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  31.  5
    Reason Out Emergence From Cellular Automata Modeling.Leilei Qi & Huaxia Zhang - 2007 - In L. Magnani & P. Li (eds.), Model-Based Reasoning in Science, Technology, and Medicine. Springer. pp. 147--159.
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  32.  4
    Evolutionary Design of Cellular Automata Using Knowledge-Based Genetic Algorithms.Daisuke Ichiba & Hitoshi Kanoh - 2006 - Transactions of the Japanese Society for Artificial Intelligence 21:103-112.
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  33. Emergent Behavior, Cellular Automata, and Our Game “Mr. Froggy”.Robert A. Henru, Narendra S. Chaudhari & Edmond C. Prakash - forthcoming - Emergence: Complexity and Organization.
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  34.  16
    Emergence and Induction of Cellular Automata Rules Via Probabilistic Reinforcement Paradigms.Burton Voorhees - 2006 - Complexity 11 (3):44-57.
  35.  2
    Emergence and Induction of Cellular Automata Rules.Burton Voorhees - 2006 - Complexity 11 (3):45.
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    Semiotics of the Artificial: The ‘Self’ of Self-Reproducing Systems in Cellular Automata.Arantza Etxeberria & Jesús Ibáñez - 1999 - Semiotica 127 (1-4):295-320.
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  37. Syntactic Autonomy, Cellular Automata, and RNA Editing: Or Why Self-Organization Needs Symbols to Evolve and How It Might Evolve Them.Luis M. Rocha - 2000 - In Jerry L. R. Chandler & Gertrudis van de Vijver (eds.), Closure: Emergent Organizations and Their Dynamics. New York Academy of Sciences. pp. 901.
     
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  38.  2
    Evolutionary Design of Cellular Automata for Noise Reduction of Grayscale Images.Shohei Sato & Hitoshi Kanoh - 2010 - Transactions of the Japanese Society for Artificial Intelligence 25 (2):311-319.
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  39.  16
    Planigon Tessellation Cellular Automata.Alexander Korobov - 1999 - Complexity 4 (6):31-38.
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  40.  23
    Brave New Modeling: Cellular Automata and Artificial Neural Networks for Mastering Complexity in Economics.Janette Aschenwald, Stefan Fink & Gottfried Tappeiner - 2001 - Complexity 7 (1):39-47.
  41.  12
    Densities and Entropies in Cellular Automata.Pierre Guillon & Charalampos Zinoviadis - 2012 - In S. Barry Cooper (ed.), How the World Computes. pp. 253--263.
  42.  8
    Dynamics of Networks Evolved for Cellular Automata Computation.Anca Gog & Camelia Chira - 2012 - In Emilio Corchado, Vaclav Snasel, Ajith Abraham, Michał Woźniak, Manuel Grana & Sung-Bae Cho (eds.), Hybrid Artificial Intelligent Systems. Springer. pp. 359--368.
  43.  18
    Some Applications of Propositional Logic to Cellular Automata.Stefano Cavagnetto - 2009 - Mathematical Logic Quarterly 55 (6):605-616.
    In this paper we give a new proof of Richardson's theorem [31]: a global function G[MATHEMATICAL DOUBLE-STRUCK CAPITAL A] of a cellular automaton [MATHEMATICAL DOUBLE-STRUCK CAPITAL A] is injective if and only if the inverse of G[MATHEMATICAL DOUBLE-STRUCK CAPITAL A] is a global function of a cellular automaton. Moreover, we show a way how to construct the inverse cellular automaton using the method of feasible interpolation from [20]. We also solve two problems regarding complexity of cellular (...)
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  44.  10
    The Operation of the Social System in a Model Based on Cellular Automata.Loet Leydesdorff - 1995 - Social Science Information 34 (3):413-441.
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  45.  27
    Islands of Stability: Engaging Emergence from Cellular Automata to the Occupy Movement.Andrew Pickering - 2014 - Zeitschrift für Medien- Und Kulturforschung 2014 (1):121-134.
    Instead of considering »being with« in terms of non-problematic, machine-like places, where reliable entities assemble in stable relationships, STS conjures up a world where the achievement of chancy stabilisations and synchronisations is local. We have to analyse how and where a certain regularity and predictability in the intersection of scientists and their instruments, say, or of human individuals and groups, is produced. The paper reviews models of emergence drawn from the history of cybernetics—the canonical »black box,« homeostats, and cellular (...)
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  46.  14
    A Solution to the Biodiversity Paradox by Logical Deterministic Cellular Automata.Vyacheslav L. Kalmykov & Lev V. Kalmykov - 2015 - Acta Biotheoretica 63 (2):203-221.
    The paradox of biological diversity is the key problem of theoretical ecology. The paradox consists in the contradiction between the competitive exclusion principle and the observed biodiversity. The principle is important as the basis for ecological theory. On a relatively simple model we show a mechanism of indefinite coexistence of complete competitors which violates the known formulations of the competitive exclusion principle. This mechanism is based on timely recovery of limiting resources and their spatio-temporal allocation between competitors. Because of limitations (...)
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  47.  3
    A Dynamic Urban Lake Area Evolution Model Based on Multilevel Grid, Cellular Automata, and Multiagent System.Jianfeng Zhu & Shenzhen Tian - 2020 - Complexity 2020:1-19.
    Urban lakes have been threatened by rapid expansion of cities in recent years. Their area changes could be extracted by remote sensing technologies. On this basis, a Dynamic Urban Lake Area Evolution Model is proposed based on a multiagent system and a cellular automata model. The DULAEM is integrated upon an Urban Lake Multilevel Grid, which is composed of the vector model with the raster model. In the DULAEM, the CA layer is mainly used for modelling the interactions (...)
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  48.  21
    Quantum Calculation of Thermal Effect in Quantum‐Dot Cellular Automata.Ioan Sturzu & Mahfuza Khatun - 2005 - Complexity 10 (4):73-78.
  49.  1
    Modeling Human Evacuating Behavior in Limited Space Based on Cellular Automata Model.Zhihong Li, Yanjie Wen, Li Zhao & Yang Dong - 2020 - Complexity 2020:1-11.
    The study of evacuation for buildings with limited space is an important part of improving evacuation efficiency and preventing stampedes. A building evacuation model was proposed based on cellular automata simulation considering different crowd states. Different flow sizes under layout environments with the same facilities as well as evacuation efficiency, bottleneck area density, and escape routes choice under the orderly and disorderly distribution conditions have also been analyzed. The results show that the initial disorderly distribution state is superior (...)
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    Structural and Functional Growth in Self‐Reproducing Cellular Automata.Eleonora Bilotta & Pietro Pantano - 2006 - Complexity 11 (6):12-29.
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