Universality of Evolved Cellular Automata In-Materio
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CSE 30151 Theory of Computing Fall 2017 Project: Cellular Automata
CSE 30151 Theory of Computing Fall 2017 Project: Cellular Automata Version 1: Jan. 17, 2018 Contents 1 Overview 2 2 History 2 3 Defining a Cellular Automata 3 3.1 State . 3 3.2 Rules . 3 3.3 Topology . 4 3.4 1D CAs . 4 4 CAs to be Implemented 4 4.1 1D CA . 5 4.1.1 Codes . 5 4.1.2 Test Cases . 5 4.2 TwoD Totalistic CA . 6 4.3 TwoD General CA . 6 5 Documentation 7 5.1 readme-team . 7 5.2 Individual Machines . 7 5.3 teamwork-netid . 8 6 Submission 8 7 Grading 9 1 1 Overview You have all probably seen Conway's \Game of Life" [3] The purpose of this project is to introduce you to a generalization of that game, termed cellular automata or CA. In this paradigm there is a possibly infinite grid of cells which interact with each other via a set of rules that use some region around each cell. Depending on the size of the team, you will implement one or more CAs of varying complexity, and design initial configurations that produce \interesting" results. These implementations will then be run with varying initial conditions, and generate time series displays of the CAs. You are free to use C, C++, Python, or even your Arduino. Python in particular has proven in the past as perhaps the most efficient way to get decent working code (the overall goal). If you use Python, feel free to use the time, csv, sys, copy, itertools, and pygame modules. Use of any other modules requires preapproval of the instructor. -
Modeling of Cellular Automata and Agent-Based Complex Systems
UNLV Theses, Dissertations, Professional Papers, and Capstones 8-1-2012 Modeling of Cellular Automata and Agent-Based Complex Systems Sara Pallekonda University of Nevada, Las Vegas Follow this and additional works at: https://digitalscholarship.unlv.edu/thesesdissertations Part of the Computer Sciences Commons Repository Citation Pallekonda, Sara, "Modeling of Cellular Automata and Agent-Based Complex Systems" (2012). UNLV Theses, Dissertations, Professional Papers, and Capstones. 1690. http://dx.doi.org/10.34917/4332671 This Thesis is protected by copyright and/or related rights. It has been brought to you by Digital Scholarship@UNLV with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in UNLV Theses, Dissertations, Professional Papers, and Capstones by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. MODELING OF CELLULAR AUTOMATA AND AGENT- BASED COMPLEX SYSTEMS by Sara S. Pallekonda A thesis submitted in partial fulfillment of the requirements for the Master of Science in Computer Science School of Computer Science Howard R. Hughes College of Engineering The Graduate College University of Nevada, Las Vegas August 2012 i THE GRADUATE COLLEGE We recommend the thesis prepared under our supervision by Sara Pallekonda entitled Modeling of Cellular Automata and Agent-Based Complex Systems be accepted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Department of Computer Science Wolfgang Bein, Committee Chair Ajoy K. -
Cellular Automata
Cellular Automata Mus270C Cellular Automata • Discrete Dynamical System – Space and 9me • Dimension – 1-D, 2-D, … • States – Neighborhood and neighbors • Rules 2 1-D Cellular Automata • Neighborhood 3 1-D Cellular Automata • Neighborhood • Black & White neighbors (States) 4 1-D Cellular Automata • Neighborhood • Black & White neighbors (States) • Rules 5 1-D Cellular Automata • Neighborhood • Black & White neighbors (States) • Rules • Example t = 1 6 1-D Cellular Automata • Neighborhood • Black & White neighbors (States) • Rules • Example t = 1 t = 2 7 1-D Cellular Automata • Neighborhood • Black & White neighbors (States) • Rules • Example t = 1 t = 2 8 1-D Cellular Automata • Neighborhood • Black & White neighbors (States) • Rules • Example t = 1 t = 2 9 1-D Cellular Automata • Neighborhood • Black & White neighbors (States) • Rules • Example t = 1 t = 2 10 1-D Cellular Automata • Neighborhood • Black & White neighbors (States) • Rules • Example t = 1 t = 2 11 Wolfram Code Every cell and its two neighbors will be one of the following types Represent a black cell with 1 and a white cell with 0 1 1 1 1 1 0 1 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 Every yellow cell above can be filled out with a 0 or a 1 giving a total of 2 8=256 possible update rules. This allows any string of eight 0s and 1’s to represent a dis9nct update rule Example: Consider the string 0 1 1 0 1 0 1 0 it can be taken to represent the update rule 1 1 1 1 1 0 1 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 Or equivalently Now think of 01101010 as the binary expansion of the number 0 27+ 1 26+ 1 25+ 0 24+1 23+ 0 22+ 1 21+ 0 20 = 64+32+8+2=106. -
Modelling with Cellular Automata
Modelling with cellular automata Modelling with cellular automata Shan He School for Computational Science University of Birmingham Module 06-23836: Computational Modelling with MATLAB Modelling with cellular automata Outline Outline of Topics Concepts about cellular automata Elementary Cellular Automaton Langton's ant Game of life CA for Lotka-Volterra model Conclusion Modelling with cellular automata Concepts about cellular automata What are cellular automata? I cellular automaton: a discrete model consists of a regular grid of cells, each in one of a finite number of states, such as \On" and “Off”. I The grid is usually in 2D, but can be in any finite number of dimensions. I The cells evolves through a number of discrete time steps according to a set of rules based on the states of neighboring cells. I Originated from von Neumann, populated in 70s' by John Conway's Game of Life, further researched by Stephen Wolfram I Can be seen as the simplest agent-based models Modelling with cellular automata Concepts about cellular automata Why CA are important? I The best tool to model and study complex system, because: I They are simple; I The mechanisms are completely known; I They can do just about anything I They might help us understand our universe: is the universe a cellular automaton? I They have lots of applications, e.g., random number generator. Modelling with cellular automata Concepts about cellular automata Some CA based biological models I Pattern formation in biology, e.g., Conus textile I Modelling cell interactions: brain tumor growth. Modelling with cellular automata Elementary Cellular Automaton Elementary Cellular Automaton I The simplest: One dimensional; I Two possible states per cell: `0' and `1'; I A cell's neighbors defined to be the adjacent cells on either side of it.