Cellular Automata Martijn van den Heuvel Models of Computation June 21st, 2011.

Slides:



Advertisements
Similar presentations
Cellular Automata (Reading: Chapter 10, Complexity: A Guided Tour)
Advertisements

1 Chapter 13 Artificial Life: Learning through Emergent Behavior.
CELLULAR AUTOMATON Presented by Rajini Singh.
CELLULAR AUTOMATA Derek Karssenberg, Utrecht University, the Netherlands LIFE (Conway)
Emulating Physics Our goal will be to show how basic dynamics known from physics can be formulated using local simple rules of cellular automata: diffusion.
CS Summer 2005 Final class - July 1st Assorted fun topics in computability and complexity.
EME Taster October Department of Computer Science EME: Emergence Module Susan Stepney Fiona Polack
Turing machine simulations. Why study so many examples? –Get an intuition for what goes on inside computers without learning all the details of a programming.
1 Lecture 13 Turing machine model of computation –Sequential access memory (tape) –Limited data types and instructions –Graphical representation –Formal.
1 Lecture 13 Turing machine model of computation –Sequential access memory (tape) –Limited data types and instructions –Formal Definition –Equivalence.
Turing Machines CS 105: Introduction to Computer Science.
Cellular Automata Orit Moskovich
Introduction to Artificial Life and Cellular Automata
Cellular Automata Avi Swartz 2015 UNC Awards Ceremony.
Introduction At the heart of the growth of a multi-cellular organism is the process of cellular division… … aka (in computing) self-replication.
A New Kind of Science Chapter 3 Matthew Ziegler CS 851 – Bio-Inspired Computing.
Nawaf M Albadia Introduction. Components. Behavior & Characteristics. Classes & Rules. Grid Dimensions. Evolving Cellular Automata using Genetic.
General Purpose 3D Cellular Automata Modeller. A Regular Lattice of Cells, each obeying the same set of rules Simple rules for individual cells can produce.
Parallelization: Conway’s Game of Life. Cellular automata: Important for science Biology – Mapping brain tumor growth Ecology – Interactions of species.
Universal Turing Machine
Generating Random Numbers in Hardware. Two types of random numbers used in computing: --”true” random numbers: ++generated from a physical source (e.g.,
Discrete Time and Discrete Event Modeling Formalisms and Their Simulators Dr. Feng Gu.
CITS4403 Computational Modelling Fractals. A fractal is a mathematical set that typically displays self-similar patterns. Fractals may be exactly the.
Artificial Chemistries – A Review Peter Dittrich, Jens Ziegler, and Wolfgang Banzhaf Artificial Life 7: , 2001 Summarized by In-Hee Lee.
Discovery of Cellular Automata Rules Using Cases Ken-ichi Maeda Chiaki Sakama Wakayama University Discovery Science 2003, Oct.17.
The Role of Artificial Life, Cellular Automata and Emergence in the study of Artificial Intelligence Ognen Spiroski CITY Liberal Studies 2005.
Computer Viruses, Artificial Life & the Origin of Life Robert C Newman Abstracts of Powerpoint Talks - newmanlib.ibri.org -newmanlib.ibri.org.
Governor’s School for the Sciences Mathematics Day 13.
CS 484 – Artificial Intelligence1 Announcements Lab 4 due today, November 8 Homework 8 due Tuesday, November 13 ½ to 1 page description of final project.
Course material – G. Tempesti Course material will generally be available the day before the lecture Includes.
1 Cellular Automata and Applications Ajith Abraham Telephone Number: (918) WWW:
Lindenmayer systems Martijn van den Heuvel May 26th, May 26th, 2011.
1 Chapter 13 Artificial Life: Learning through Emergent Behavior.
Introduction to Lattice Simulations. Cellular Automata What are Cellular Automata or CA? A cellular automata is a discrete model used to study a range.
Playing God: The Engineering of Functional Designs in the Game of Life Liban Mohamed Computer Systems Research Lab
EASy Summer 2006Non Symbolic AI Lecture 131 Non Symbolic AI - Lecture 13 Symbolic AI is often associated with the idea that “ Intelligence is Computation”
Cellular Automata Spatio-Temporal Information for Society Münster, 2014.
Cellular Automata & DNA Computing 우정철. Definition Of Cellular Automata Von Von Neuman’s Neuman’s Definition Wolfram’s Wolfram’s Definition Lyman.
CELLULAR AUTOMATA A Presentation By CSC. OUTLINE History One Dimension CA Two Dimension CA Totalistic CA & Conway’s Game of Life Classification of CA.
REVERSIBLE CELLULAR AUTOMATA WITHOUT MEMORY Theofanis Raptis Computational Applications Group Division of Applied Technologies NCSR Demokritos, Ag. Paraskevi,
Using Evolutionary Computation as a Creativity-Support Tool Tim ChabukUniversity of Maryland Jason LohnCarnegie Mellon University Derek LindenX5 Systems.
Model Iteration Iteration means to repeat a process and is sometimes referred to as looping. In ModelBuilder, you can use iteration to cause the entire.
Cellular Automata. John von Neumann 1903 – 1957 “a Hungarian-American mathematician and polymath who made major contributions to a vast number of fields,
4th International Conference on High Performance Scientific Computing 4th International Conference on High Performance Scientific Computing A Framework.
Cellular Automata FRES 1010 Eileen Kraemer Fall 2005.
Cellular Automata Introduction  Cellular Automata originally devised in the late 1940s by Stan Ulam (a mathematician) and John von Neumann.  Originally.
Cellular Automata Martijn van den Heuvel Models of Computation June 21st, 2011.
Cellular Automata BIOL/CMSC 361: Emergence 2/12/08.
Pedro R. Andrade Münster, 2013
제 4 주. Cellular Automata A Brief history of Cellular Automata P. Sarkar, ACM Computing Surveys, vol. 32, no. 1, pp. 80~107, 2000 학습목표 계산도구로서의 Cellular.
Computational Mechanics of ECAs, and Machine Metrics.
Conway’s Game of Life Jess Barak Game Theory. History Invented by John Conway in 1970 Wanted to simplify problem from 1940s presented by John von Neumann.
1 8.4 Extensions to the Basic TM Extended TM’s to be studied: Multitape Turing machine Nondeterministic Turing machine The above extensions make no increase.
MA/CSSE 474 Theory of Computation Universal Turing Machine Church-Turing Thesis (Winter 2016, these slides were also used for Day 33)
MA/CSSE 474 Theory of Computation Universal Turing Machine Church-Turing Thesis Delayed due dates for HWs See updated schedule page. No class meeting.
Chaotic Behavior - Cellular automata
Spatio-Temporal Information for Society Münster, 2014
Hiroki Sayama NECSI Summer School 2008 Week 3: Methods for the Study of Complex Systems Cellular Automata Hiroki Sayama
Illustrations of Simple Cellular Automata
OTHER MODELS OF TURING MACHINES
Alexei Fedorov January, 2011
Cellular Automata.
Pedro R. Andrade Münster, 2013
Theory of Computation Turing Machines.
Spatio-temporal information in society: cellular automata
Hiroki Sayama NECSI Summer School 2008 Week 2: Complex Systems Modeling and Networks Cellular Automata Hiroki Sayama
Excursions into Logic Based Computation using Conway’s Game of Life
Cellular Automata (CA) Overview
Variations: Multiple tracks Multiple tapes Non-deterministic
Complexity as Fitness for Evolved Cellular Automata Update Rules
Presentation transcript:

Cellular Automata Martijn van den Heuvel Models of Computation June 21st, 2011

Overview History Formal description Elementary CA Example of a CA Turing completeness Lindenmayer Systems

History 1940s  Von Neumann - self-replicating machines 1970s  Conway – Game of Life 1980s  Wolfram – Investigating properties 2000s  Cook – Proves Turing completeness

Use Visualizing processes Simple components  complex behavior  Fluid dynamics  Biological pattern formation  Traffic models  Artifical life

Formal description Cellular space  Lattice/grid of N identical cells  Cell i at time t has a state s i t  Cell i at time t has a neighborhood n i t Transition rules  r(n i t ) updates cell i to next state s i t+1  Size of rule set: |R|=|s| |n| 2 3 =8 Simultaneous updating Rule000  1

Elementary CA Simplest form of CA  One-dimensional  Binary states {0,1}  2 neighbors 2 3 =8 possible configurations 2 8 =256 different elementary CAs out0,1 s

Example nitnit s i t Example Java Applet

Turing completeness Matthew Cook Rule 110 Emulation of Post Tag System  Gliders/Spaceships interacting  Structures representing: Infinite data string Infinitely repeating set of production rules Output nitnit s i t

Turing completeness

Lindenmayer systems Extensions States: { a,b,c,d,k } Rules: a  cbc b  dad c  k d  a k  k acbckdadkkacbcakkcbckdadkcbck

L-systems vs CA Both:  Loop until no rule is applicable  States for data storage  Transition rules for modification  Use simultaneous rule application Differences:  L-system usually has non-binary states  L-system rewrites multiple states in one step CA updates only cell i

L-systems vs CA Possible solutions:  Abstract L-system Allow same rules as CA  Extend CA  multiple states Rule table grows fast CA could write sequentially what L-system does in one step

L-systems vs CA Butler  Simulate D(m,n)L-system on a 1-dim CA  Uses registers (m per cell) containing: Direction  or  ‘unprocessed’ states

Butler CA: L-system to be simulated: Axiom: 1 Rules: 0  00 1 

Butler Using registers as neighborhood  Still 1-dimensional? Extending CA beyond original properties? Very simple L-system L-system is an extended CA

Conclusion CA and L-system function in nearly the same way  Using states & transition rules Both can simulate a TM (indirectly)  CA becomes complex and hard to interpret Both apply rules as long as possible, then stop. Both apply rules simultaneously L-system could be seen as an extended CA

Reading Wolfram's publications Simulation of TM on a CA  Universality in Elementary Cellular Automata Matthew Cook; Complex Systems 15 (2004) p.1-40