Swarm Intelligence Systems
|
|
- Austen Shelton
- 6 years ago
- Views:
Transcription
1 Swarm Intelligence Systems Christian Jacob Department of Computer Science University of Calgary
2 Cellular Automata Global Effects from Local Rules
3 Cellular Automata The CA space is a lattice of cells with a particular geometry. Each cell contains a variable from a limited range (e.g., 0 and 1). All cells update synchronously. All cells use the same updating rule, depending only on local relations. Time advances in discrete steps. 3
4 One-dimensional finite CA architecture K = 5 local connections per cell Synchronous update in discrete time steps time A. Wuensche: The Ghost in the Machine, Artificial Life III,
5 Cellular Automata: Local Rules Global Effects --> 1D-CA Demos 5
6 Time Evolution of the ith Cell ( t + 1) i () t () t () t () t () t i [ K / 2] i 1 i i+ 1 i+ [ K / 2] C = f( C,..., C, C, C,..., C ) With periodic boundary conditions: x < 1: Cx = CN+ x x > N: C = C N x x 6
7 Value Range and Update Rules For V different states (= values) per cell there are V K permuations of values in a neighbourhood of size K. The update function f can be implemented as a lookup table with V K entries, giving V VK possible rules. 7
8 Example Update Rule V = 2, K = 3 The rule table for rule 30: See examples... 8
9 2-D CA: Emergent Pattern Formation in Excitable Media Neuron excitation Hodgepodge Neuron excitation (relaxed) 9
10 Cellular Automata Swarm Systems Random Boolean Networks Classifier Systems 10
11 Ants Hölldobler & Wilson, 1990
12 Self-organization Team work Competition... and Heavy Loads Hölldobler & Wilson, 1990
13 Teamwork Hölldobler & Wilson, 1990 Living Architecture
14 Living Network Living Bridge Hölldobler & Wilson, 1990
15 Together we are strong... Hölldobler & Wilson, 1990
16 Ant Foraging Behaviour Learning about Emergent System Behaviours
17 Ant Foreaging and Shortest Paths Experimental setup for studying ant foreaging behaviour Bonabeau et al., 1999
18 Shortest Path Discovery (a) Ants walking between nest and food sites (b) An obstacle is placed in the middle. (c) Ants turn left or right, while droping pheromone... (d) and finally the shortest path emerges.
19 Adaptation to Environmental Changes (a) The newly found shortest path (b) Moving the obstacle (c) Discovery of new shortest path
20 Massively Parallel Micro Worlds StarLogo Mitchel Resnick (MIT, 1997)
21 Agent-Based Evolution Massive Parallelism Interacting Agents Cooperation Competition Emergent System Behaviour StarLogo Demo
22 Emergent System Behaviour Simulated Ant Foraging Collective Foraging Equidistant Food Sites Randomly Distributed Food Sites
23 Emergent System Behaviour Simulated Ant Foraging to look-for-food if not carrying-food? [ifelse (ask patch-here [pheromone]) < 0.2 [right random 40 left random 40] [set-heading uphill pheromone] forward 1] end to find-food if (not carrying-food?) and ask patch-here [food > 0] [set-carrying-food? True ask patch-here [set-food food - 1] set-drop-size 35 right 180 forward 1] end to return-to-nest if carrying-food? [ask patch-here [add-pheromone-drop] set-drop-size drop-size set-heading uphill nest-scent forward 1] end to find-nest if carrying-food? and ask patch-here [nest?] [set-carrying-food? False right 180 forward 1] end
24 Demo Following Behaviour
25 Interactions among Social Insects
26 Interactions among Social Insects Direct Interactions Food or liquid exchange Visual or tactile, or scentuous contact Indirect Interactions: Stigmergy Pheromones Individual behaviour modifies the environment (e.g., by putting up signs = stigma), which in turn modifies the behaviour of other individuals.
27 Demo Shepherds and Sheep
28 Demo Stigmergy in Action Bonabeau et al., 1999
29 Complex Systems Emergent Behaviours and Patterns from Local Interactions
30 Stevens et al., 1988
31 Nuridsany & Pérennou, 1996
32 Nuridsany & Pérennou, 1996 Ernst, 1998
33 What to Learn from Ant Colonies as Complex Systems Fairly simple units generate complicated global behaviour. If we knew how an ant colony works, we might understand more about how all such systems work, from brains to ecosystems. (Gordon, 1999)
34 Emergence in Complex Systems How do neurons respond to each other in a way that produces thoughts? How do cells respond to each other in a way that produces the distinct tissues of a growing embryo? How do species interact to produce predictable changes,, over time, in ecological communities?...
35 Swarm Systems Providing New Insights...
36 References Bonabeau, E., Dorigo, M., and Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. New York, Oxford University Press. Ernst, A. M., ed. (1998). Digest: Kooperation und Konkurrenz, Heidelberg, Spektrum Akademischer Verlag. Gordon, D. (1999). Ants at Work. New York, The Free Press. Hölldobler, B., and Wilson, E. O. (1990). The Ants. Cambridge, MA, Harvard University Press. Nuridsany, C., and Pérennou, M. (1996). Microcosmos: The Invisible World of Insects. New York, Stewart, Tabori & Chang. Resnik, M. (1997). Turtles, Termites, and Traffic Jams. Cambridge, MA, MIT Press. Stevens, C. F., et al. (1988). Gehirn und Nervensystem. Heidelberg, Spektrum Akademischer Verlag.
depending only on local relations. All cells use the same updating rule, Time advances in discrete steps. All cells update synchronously.
Swarm Intelligence Systems Cellular Automata Christian Jacob jacob@cpsc.ucalgary.ca Global Effects from Local Rules Department of Computer Science University of Calgary Cellular Automata One-dimensional
More informationToward a Better Understanding of Complexity
Toward a Better Understanding of Complexity Definitions of Complexity, Cellular Automata as Models of Complexity, Random Boolean Networks Christian Jacob jacob@cpsc.ucalgary.ca Department of Computer Science
More informationCellular Automata. and beyond. The World of Simple Programs. Christian Jacob
Cellular Automata and beyond The World of Simple Programs Christian Jacob Department of Computer Science Department of Biochemistry & Molecular Biology University of Calgary CPSC / MDSC 605 Fall 2003 Cellular
More informationCellular Automata. ,C ) (t ) ,..., C i +[ K / 2] Cellular Automata. x > N : C x ! N. = C x. x < 1: C x. = C N+ x.
and beyond Lindenmayer Systems The World of Simple Programs Christian Jacob Department of Computer Science Department of Biochemistry & Molecular Biology University of Calgary CPSC 673 Winter 2004 Random
More informationContact Information CS 420/527. Biologically-Inspired Computation. CS 420 vs. CS 527. Grading. Prerequisites. Textbook 1/11/12
CS 420/527 Biologically-Inspired Computation Bruce MacLennan web.eecs.utk.edu/~mclennan/classes/420 Contact Information Instructor: Bruce MacLennan maclennan@eecs.utk.edu Min Kao 425 Office Hours: 3:30
More informationContact Information. CS 420/594 (Advanced Topics in Machine Intelligence) Biologically-Inspired Computation. Grading. CS 420 vs. CS 594.
CS 420/594 (Advanced Topics in Machine Intelligence) Biologically-Inspired Computation Bruce MacLennan http://www.cs.utk.edu/~mclennan/classes/420 Contact Information Instructor: Bruce MacLennan maclennan@eecs.utk.edu
More informationIntroduction to Swarm Robotics
COMP 4766 Introduction to Autonomous Robotics Introduction to Swarm Robotics By Andrew Vardy April 1, 2014 Outline 1 Initial Definitions 2 Examples of SI in Biology 3 Self-Organization 4 Stigmergy 5 Swarm
More informationOutline. 1 Initial Definitions. 2 Examples of SI in Biology. 3 Self-Organization. 4 Stigmergy. 5 Swarm Robotics
Outline COMP 4766 Introduction to Autonomous Robotics 1 Initial Definitions Introduction to Swarm Robotics 2 Examples of SI in Biology 3 Self-Organization By Andrew Vardy 4 Stigmergy April 1, 2014 5 Swarm
More informationAvailable online at ScienceDirect. Procedia Computer Science 20 (2013 ) 90 95
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 20 (2013 ) 90 95 Complex Adaptive Systems, Publication 3 Cihan H. Dagli, Editor in Chief Conference Organized by Missouri
More informationCapacitor Placement for Economical Electrical Systems using Ant Colony Search Algorithm
Capacitor Placement for Economical Electrical Systems using Ant Colony Search Algorithm Bharat Solanki Abstract The optimal capacitor placement problem involves determination of the location, number, type
More informationSensitive Ant Model for Combinatorial Optimization
Sensitive Ant Model for Combinatorial Optimization CAMELIA CHIRA cchira@cs.ubbcluj.ro D. DUMITRESCU ddumitr@cs.ubbcluj.ro CAMELIA-MIHAELA PINTEA cmpintea@cs.ubbcluj.ro Abstract: A combinatorial optimization
More informationVI" Autonomous Agents" &" Self-Organization! Part A" Nest Building! Autonomous Agent! Nest Building by Termites" (Natural and Artificial)!
VI" Autonomous Agents" &" Self-Organization! Part A" Nest Building! 1! 2! Autonomous Agent!! a unit that interacts with its environment " (which probably consists of other agents)!! but acts independently
More informationAnt Foraging Revisited
Ant Foraging Revisited Liviu A. Panait and Sean Luke George Mason University, Fairfax, VA 22030 lpanait@cs.gmu.edu, sean@cs.gmu.edu Abstract Most previous artificial ant foraging algorithms have to date
More informationMeta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model
Chapter -7 Meta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model 7.1 Summary Forecasting summer monsoon rainfall with
More informationAn ant colony algorithm for multiple sequence alignment in bioinformatics
An ant colony algorithm for multiple sequence alignment in bioinformatics Jonathan Moss and Colin G. Johnson Computing Laboratory University of Kent at Canterbury Canterbury, Kent, CT2 7NF, England. C.G.Johnson@ukc.ac.uk
More informationSwarm-bots and Swarmanoid: Two experiments in embodied swarm intelligence
Swarm-bots and Swarmanoid: Two experiments in embodied swarm intelligence Marco Dorigo FNRS Research Director IRIDIA Université Libre de Bruxelles IAT - 17.9.2009 - Milano, Italy What is swarm intelligence?
More informationModels of Termite Nest Construction Daniel Ladley Computer Science 2003/2004
Models of Termite Nest Construction Daniel Ladley Computer Science 2003/2004 The candidate confirms that the work submitted is their own and the appropriate credit has been given where reference has been
More informationARTIFICIAL INTELLIGENCE
BABEŞ-BOLYAI UNIVERSITY Faculty of Computer Science and Mathematics ARTIFICIAL INTELLIGENCE Solving search problems Informed local search strategies Nature-inspired algorithms March, 2017 2 Topics A. Short
More informationDRAFT VERSION: Simulation of Cooperative Control System Tasks using Hedonistic Multi-agents
DRAFT VERSION: Simulation of Cooperative Control System Tasks using Hedonistic Multi-agents Michael Helm, Daniel Cooke, Klaus Becker, Larry Pyeatt, and Nelson Rushton Texas Tech University, Lubbock Texas
More informationAnt Colony Optimization: an introduction. Daniel Chivilikhin
Ant Colony Optimization: an introduction Daniel Chivilikhin 03.04.2013 Outline 1. Biological inspiration of ACO 2. Solving NP-hard combinatorial problems 3. The ACO metaheuristic 4. ACO for the Traveling
More informationEmergent Teamwork. Craig Reynolds. Cognitive Animation Workshop June 4-5, 2008 Yosemite
Emergent Teamwork Craig Reynolds Cognitive Animation Workshop June 4-5, 2008 Yosemite 1 This talk Whirlwind tour of collective behavior in nature Some earlier simulation models Recent work on agent-based
More informationDecrease in Number of Piles. Lecture 10. Why does the number of piles decrease? More Termites. An Experiment to Make the Number Decrease More Quickly
Decrease in Number of Piles Lecture 10 9/25/07 1 9/25/07 2 Why does the number of piles decrease? A pile can grow or shrink But once the last chip is taken from a pile, it can never restart Is there any
More informationSwarm Intelligence W13: From Aggregation and Segregation to Structure Building
Swarm Intelligence W13: From Aggregation and Segregation to Structure Building Stigmergy Quantitative Qualitative Outline Distributed building in: natural systems artificial virtual systems artificial
More informationOptimal Shape and Topology of Structure Searched by Ants Foraging Behavior
ISSN 0386-1678 Report of the Research Institute of Industrial Technology, Nihon University Number 83, 2006 Optimal Shape and Topology of Structure Searched by Ants Foraging Behavior Kazuo MITSUI* ( Received
More informationAnt Algorithms. Ant Algorithms. Ant Algorithms. Ant Algorithms. G5BAIM Artificial Intelligence Methods. Finally. Ant Algorithms.
G5BAIM Genetic Algorithms G5BAIM Artificial Intelligence Methods Dr. Rong Qu Finally.. So that s why we ve been getting pictures of ants all this time!!!! Guy Theraulaz Ants are practically blind but they
More informationM odeling and sim ula ting the forag ing system in multi2source groups w ith random d isturbances
3 4 Vol. 3. 4 20088 CAA I Transactions on Intelligent System s Aug. 2008,, (,150001) :..... 2,,,. Starlogo,.,. : ;; ; Starlogo : TP18 : A: 167324785 (2008) 0420342207 M odeling and sim ula ting the forag
More informationAgent-Based Modeling Using Swarm Intelligence in Geographical Information Systems
Agent-Based Modeling Using Swarm Intelligence in Geographical Information Systems Rawan Ghnemat, Cyrille Bertelle, Gérard H.E. Duchamp To cite this version: Rawan Ghnemat, Cyrille Bertelle, Gérard H.E.
More informationASGA: Improving the Ant System by Integration with Genetic Algorithms
ASGA: Improving the Ant System by Integration with Genetic Algorithms Tony White, Bernard Pagurek, Franz Oppacher Systems and Computer Engineering, Carleton University, 25 Colonel By Drive, Ottawa, Ontario,
More informationTowards Synthesizing Artificial Neural Networks that Exhibit Cooperative Intelligent Behavior: Some Open Issues in Artificial Life Michael G.
Towards Synthesizing Artificial Neural Networks that Exhibit Cooperative Intelligent Behavior: Some Open Issues in Artificial Life Michael G. Dyer Computer Science Department, UCLA Overview Introduction
More informationSelf-Adaptive Ant Colony System for the Traveling Salesman Problem
Proceedings of the 29 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 29 Self-Adaptive Ant Colony System for the Traveling Salesman Problem Wei-jie Yu, Xiao-min
More informationEmergent Computing for Natural and Social Complex Systems
Emergent Computing for Natural and Social Complex Systems Professor Cyrille Bertelle http:\\litislab.univ-lehavre.fr\ bertelle LITIS - Laboratory of Computer Sciences, Information Technologies and Systemic
More informationOutline. Ant Colony Optimization. Outline. Swarm Intelligence DM812 METAHEURISTICS. 1. Ant Colony Optimization Context Inspiration from Nature
DM812 METAHEURISTICS Outline Lecture 8 http://www.aco-metaheuristic.org/ 1. 2. 3. Marco Chiarandini Department of Mathematics and Computer Science University of Southern Denmark, Odense, Denmark
More informationComputational Models of Complex Systems
CS 790R Seminar Modeling & Simulation Computational Models of Complex Systems ~ Introductory Lecture 2 ~ René Doursat Department of Computer Science & Engineering University of Nevada, Reno Spring 2005
More informationTemplates. Template is a pattern used to construct another pattern Used in conjunction with sorting behaviour:
Templates Template is a pattern used to construct another pattern Used in conjunction with sorting behaviour: Predictable behaviour Parametric Example: Define number and location of clusters Move to cluster,
More informationSelf-Organization in Social Insects
Self-Organization in Social Insects Kazuo Uyehara 5/04/2008 Self-organization is a process in which complex patterns or behaviors are produced without direction from an outside or supervising source. This
More informationEvolving Agent Swarms for Clustering and Sorting
Evolving Agent Swarms for Clustering and Sorting Vegard Hartmann Complex Adaptive Organically-Inspired Systems Group (CAOS) Dept of Computer Science The Norwegian University of Science and Technology (NTNU)
More informationImplementation of Travelling Salesman Problem Using ant Colony Optimization
RESEARCH ARTICLE OPEN ACCESS Implementation of Travelling Salesman Problem Using ant Colony Optimization Gaurav Singh, Rashi Mehta, Sonigoswami, Sapna Katiyar* ABES Institute of Technology, NH-24, Vay
More informationbiologically-inspired computing lecture 22 Informatics luis rocha 2015 INDIANA UNIVERSITY biologically Inspired computing
lecture 22 -inspired Sections I485/H400 course outlook Assignments: 35% Students will complete 4/5 assignments based on algorithms presented in class Lab meets in I1 (West) 109 on Lab Wednesdays Lab 0
More informationDeterministic Nonlinear Modeling of Ant Algorithm with Logistic Multi-Agent System
Deterministic Nonlinear Modeling of Ant Algorithm with Logistic Multi-Agent System Rodolphe Charrier, Christine Bourjot, François Charpillet To cite this version: Rodolphe Charrier, Christine Bourjot,
More informationAgent-based models of animal behaviour From micro to macro and back.
Agent-based models of animal behaviour From micro to macro and back. Ellen Evers, Behavioural Biology, University Utrecht. Outline... - genome cell organism group population ecosystem -... Organization,
More informationBioinspired Environmental Coordination in Spatial Computing Systems
Bioinspired Environmental Coordination in Spatial Computing Systems Justin Werfel jkwerfel@mit.edu Yaneer Bar-Yam yaneer@necsi.edu Donald Ingber donald.ingber@childrens.harvard.edu New England Complex
More informationCharacterization of Fixed Points in Sequential Dynamical Systems
Characterization of Fixed Points in Sequential Dynamical Systems James M. W. Duvall Virginia Polytechnic Institute and State University Department of Mathematics Abstract Graph dynamical systems are central
More informationComputer Simulations
Computer Simulations A practical approach to simulation Semra Gündüç gunduc@ankara.edu.tr Ankara University Faculty of Engineering, Department of Computer Engineering 2014-2015 Spring Term Ankara University
More informationEvolution of Altruistic Robots
Evolution of Altruistic Robots Dario Floreano 1, Sara Mitri 1, Andres Perez-Uribe 2, Laurent Keller 3 1 Laboratory of Intelligent Systems, EPFL, Lausanne, Switzerland 2 University of Applied Sciences,
More informationSwarm-driven idea models From insect nests to modern architecture
Swarm-driven idea models From insect nests to modern architecture Sebastian von Mammen and Christian Jacob Department of Computer Science, University of Calgary, Canada Abstract Inspired by the construction
More informationModelling the Role of Trail Pheromone in the Collective Construction of Termite Royal Chambers
Modelling the Role of Trail Pheromone in the Collective Construction of Termite Royal Chambers Nicholas Hill 1 and Seth Bullock Institute for Complex Systems Simulation, University of Southampton, UK,
More informationCollective Decision-Making in Honey Bee Foraging Dynamics
Collective Decision-Making in Honey Bee Foraging Dynamics Valery Tereshko and Andreas Loengarov School of Computing, University of Paisley, Paisley PA1 2BE, Scotland October 13, 2005 Abstract We consider
More informationSwarm intelligence: Ant Colony Optimisation
Swarm intelligence: Ant Colony Optimisation S Luz luzs@cs.tcd.ie October 7, 2014 Simulating problem solving? Can simulation be used to improve distributed (agent-based) problem solving algorithms? Yes:
More informationSpatial Simulations with Cognitive and Design Agents
Spatial Simulations with Cognitive and Design Agents Renee Puusepp e-mail: renee.puusepp@sliderstudio.co.uk Paul Coates e-mail: P.S.Coates@uel.ac.uk CECA, AVA, University of East London 4-6 University
More informationOrganization of work via the "common stomach" in social insects
Organization of work via the "common stomach" in social insects István Karsai*, Thomas Schmickl** *Dept. Biological Sciences East Tennessee State University **Dept. Zoology Karl Franzens University Graz
More informationSimAnt Simulation Using NEAT
SimAnt Simulation Using NEAT Erin Gluck and Evelyn Wightman December 15, 2015 Abstract In nature, ants in a colony often work together to complete a common task. We want to see if we can simulate this
More informationComplex Systems Made Simple
Complex Systems Made Simple 1. Introduction 2. A Complex Systems Sampler a. Cellular automata b. Pattern formation c. Swarm intelligence d. Complex networks e. Spatial communities f. Structured morphogenesis
More informationComplex Systems Theory and Evolution
Complex Systems Theory and Evolution Melanie Mitchell and Mark Newman Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501 In Encyclopedia of Evolution (M. Pagel, editor), New York: Oxford University
More informationCoevolution of Multiagent Systems using NEAT
Coevolution of Multiagent Systems using NEAT Matt Baer Abstract This experiment attempts to use NeuroEvolution of Augmenting Topologies (NEAT) create a Multiagent System that coevolves cooperative learning
More informationCellular Automata Models of Pedestrian Dynamics
Cellular Automata Models of Pedestrian Dynamics Andreas Schadschneider Institute for Theoretical Physics University of Cologne Germany www.thp.uni-koeln.de/~as www.thp.uni-koeln.de/ant-traffic Overview
More informationStigmergy: a fundamental paradigm for digital ecosystems?
Stigmergy: a fundamental paradigm for digital ecosystems? Francis Heylighen Evolution, Complexity and Cognition group Vrije Universiteit Brussel 1 Digital Ecosystem Complex, self-organizing system Agents:
More informationComputational Intelligence Methods
Computational Intelligence Methods Ant Colony Optimization, Partical Swarm Optimization Pavel Kordík, Martin Šlapák Katedra teoretické informatiky FIT České vysoké učení technické v Praze MI-MVI, ZS 2011/12,
More informationEnvironmental signals
Environmental signals Why are environmental signals rare? Pp 632-635 Resource recruitment signals Costs and benefits Vertebrates and social insects Predator detection signals Types Patterns of usage Intertrophic
More informationMeta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model
Meta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model Presented by Sayantika Goswami 1 Introduction Indian summer
More informationOptimal Traffic Organisation in Ants under Crowded Conditions.
Optimal Traffic Organisation in Ants under Crowded Conditions. Audrey Dussutour *, Vincent Fourcassié *, Dirk Helbing #, Jean-Louis Deneubourg * Centre de Recherches sur la Cognition Animale, UMR CNRS
More informationStarlogo and its Relatives
Logo (Papert) Starlogo and its Relatives Language for teaching mathematics graphically Tell turtle how to move Starlogo (Resnick) & StarlogoT (Wilensky) Many turtles Tell turtles how to interact with each
More informationSwarm-bots. Marco Dorigo FNRS Research Director IRIDIA Université Libre de Bruxelles
Swarm-bots Marco Dorigo FNRS Research Director IRIDIA Université Libre de Bruxelles Swarm-bots The swarm-bot is an experiment in swarm robotics Swarm robotics is the application of swarm intelligence principles
More informationλ-universe: Introduction and Preliminary Study
λ-universe: Introduction and Preliminary Study ABDOLREZA JOGHATAIE CE College Sharif University of Technology Azadi Avenue, Tehran IRAN Abstract: - Interactions between the members of an imaginary universe,
More informationFemtosecond Quantum Control for Quantum Computing and Quantum Networks. Caroline Gollub
Femtosecond Quantum Control for Quantum Computing and Quantum Networks Caroline Gollub Outline Quantum Control Quantum Computing with Vibrational Qubits Concept & IR gates Raman Quantum Computing Control
More informationA Land Use Spatial Allocation Model based on Ant. Colony Optimization
A Land Use Spatial Allocation Model based on Ant Colony Optimization LIU YaoLin 1,2 *, TANG DiWei 1,LIU DianFeng 1,2,KONG XueSong 1,2 1 School of Resource and Environment Science, Wuhan University, Wuhan
More information2015 Todd Neller. A.I.M.A. text figures 1995 Prentice Hall. Used by permission. Neural Networks. Todd W. Neller
2015 Todd Neller. A.I.M.A. text figures 1995 Prentice Hall. Used by permission. Neural Networks Todd W. Neller Machine Learning Learning is such an important part of what we consider "intelligence" that
More informationStarLogo Simulation of Streaming Aggregation. Demonstration of StarLogo Simulation of Streaming. Differentiation & Pattern Formation.
StarLogo Simulation of Streaming Aggregation 1. chemical diffuses 2. if cell is refractory (yellow) 3. then chemical degrades 4. else (it s excitable, colored white) 1. if chemical > movement threshold
More informationIntuitionistic Fuzzy Estimation of the Ant Methodology
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 9, No 2 Sofia 2009 Intuitionistic Fuzzy Estimation of the Ant Methodology S Fidanova, P Marinov Institute of Parallel Processing,
More informationFUNDAMENTALS OF NATURAL COMPUTING Basic Concepts, Algorithms, and Applications
FUNDAMENTALS OF NATURAL COMPUTING Basic Concepts, Algorithms, and Applications Leandro Nunes de Castro Catholic University of Santos (UniSantos) Brazil Chapman &. Hall/CRC Taylor &. Francis Croup Boca
More information28 3 Insects Slide 1 of 44
1 of 44 Class Insecta contains more species than any other group of animals. 2 of 44 What Is an Insect? What Is an Insect? Insects have a body divided into three parts head, thorax, and abdomen. Three
More informationMeasures of Work in Artificial Life
Measures of Work in Artificial Life Manoj Gambhir, Stephen Guerin, Daniel Kunkle and Richard Harris RedfishGroup, 624 Agua Fria Street, Santa Fe, NM 87501 {manoj, stephen, daniel, rich}@redfish.com Abstract
More informationEusocial species. Eusociality. Phylogeny showing only eusociality Eusocial insects. Eusociality: Cooperation to the extreme
Eusociality: Cooperation to the extreme Groups form colonies with reproductive and worker castes. Eusociality has evolved most often in insects: Ants Eusocial species Honeybees Termites Wasps Phylogeny
More informationProbabilistic Model Checking of Ant-Based Positionless Swarming
Probabilistic Model Checking of Ant-Based Positionless Swarming Paul Gainer, Clare ixon, and Ullrich Hustadt epartment of Computer Science, University of Liverpool Liverpool, L9 BX United Kingdom P.Gainer,
More informationCan You do Maths in a Crowd? Chris Budd
Can You do Maths in a Crowd? Chris Budd Human beings are social animals We usually have to make decisions in the context of interactions with many other individuals Examples Crowds in a sports stadium
More informationMining Spatial Trends by a Colony of Cooperative Ant Agents
Mining Spatial Trends by a Colony of Cooperative Ant Agents Ashan Zarnani Masoud Rahgozar Abstract Large amounts of spatially referenced data has been aggregated in various application domains such as
More informationTitle: Excavation and aggregation as organizing factors in de novo construction by moundbuilding
Supplemental Information Title: Excavation and aggregation as organizing factors in de novo construction by moundbuilding termites Authors: Ben Green, Paul Bardunias, J. Scott Turner, Radhika Nagpal, Justin
More informationArtificial Ecosystems for Creative Discovery
Artificial Ecosystems for Creative Discovery Jon McCormack Centre for Electronic Media Art Monash University Clayton 3800, Australia www.csse.monash.edu.au/~jonmc Jon.McCormack@infotech.monash.edu.au 1
More informationbiologically-inspired computing lecture 5 Informatics luis rocha 2015 biologically Inspired computing INDIANA UNIVERSITY
lecture 5 -inspired Sections I485/H400 course outlook Assignments: 35% Students will complete 4/5 assignments based on algorithms presented in class Lab meets in I1 (West) 109 on Lab Wednesdays Lab 0 :
More informationCreative Genomic Webs -Kapil Rajaraman PHY 498BIO, HW 4
Creative Genomic Webs -Kapil Rajaraman (rajaramn@uiuc.edu) PHY 498BIO, HW 4 Evolutionary progress is generally considered a result of successful accumulation of mistakes in replication of the genetic code.
More informationSYMPOSIUM Student Journal of Science & Math. Volume 2 Issue 1
SYMPOSIUM Student Journal of Science & Math Volume 2 Issue 1 biology 117 B82.731 OBSERVATIONAL LEARNING IN EUSOCIAL INSECTS Background A RESEARCH PROPOSAL by Avity Norman Ants (order Hymenoptera, family
More informationChapter 17: Ant Algorithms
Computational Intelligence: Second Edition Contents Some Facts Ants appeared on earth some 100 million years ago The total ant population is estimated at 10 16 individuals [10] The total weight of ants
More informationbiologically-inspired computing lecture 18
Informatics -inspired lecture 18 Sections I485/H400 course outlook Assignments: 35% Students will complete 4/5 assignments based on algorithms presented in class Lab meets in I1 (West) 109 on Lab Wednesdays
More informationSC741 W12: Division of Labor Part I: Fixed- and Variable- Threshold Algorithms
SC741 W12: Division of Labor Part I: Fixed- and Variable- Threshold Algorithms Outline Division of labor in natural systems Ants Bees, wasps Models and mechanisms Fixed-threshold mechanisms Variable-threshold
More informationLearning Cellular Automaton Dynamics with Neural Networks
Learning Cellular Automaton Dynamics with Neural Networks N H Wulff* and J A Hertz t CONNECT, the Niels Bohr Institute and Nordita Blegdamsvej 17, DK-2100 Copenhagen 0, Denmark Abstract We have trained
More informationEvolutionary Games and Computer Simulations
Evolutionary Games and Computer Simulations Bernardo A. Huberman and Natalie S. Glance Dynamics of Computation Group Xerox Palo Alto Research Center Palo Alto, CA 94304 Abstract The prisoner s dilemma
More informationThe application of swarm intelligence to the problem of automatic plan generation
EMERGENT SPACE DIAGRAMS The application of swarm intelligence to the problem of automatic plan generation TIM IRELAND Bartlett School of Graduate Studies, University College London, UK abstract: This work
More informationChapter 1 Introduction
Chapter 1 Introduction 1.1 Introduction to Chapter This chapter starts by describing the problems addressed by the project. The aims and objectives of the research are outlined and novel ideas discovered
More informationIntroduction and Perceptron Learning
Artificial Neural Networks Introduction and Perceptron Learning CPSC 565 Winter 2003 Christian Jacob Department of Computer Science University of Calgary Canada CPSC 565 - Winter 2003 - Emergent Computing
More informationTraffic Signal Control with Swarm Intelligence
009 Fifth International Conference on Natural Computation Traffic Signal Control with Swarm Intelligence David Renfrew, Xiao-Hua Yu Department of Electrical Engineering, California Polytechnic State University
More informationStigmergic navigation on an RFID floor with a multi-robot team Ali Abdul Khaliq Maurizio Di Rocco Alessandro Saffiotti
Stigmergic navigation on an RFID floor with a multi-robot team Ali Abdul Khaliq Maurizio Di Rocco Alessandro Saffiotti AASS Cognitive Robotic Systems Lab University of Örebro, Sweden 1 Motivation Stigmergy
More informationAgent Based Modeling and Simulation in the Social Sciences. Frank Witmer Computing and Research Services 14 May 2014
Agent Based Modeling and Simulation in the Social Sciences Frank Witmer Computing and Research Services 14 May 2014 Outline What are agent based models? How do they work? What types of questions are they
More information3D HP Protein Folding Problem using Ant Algorithm
3D HP Protein Folding Problem using Ant Algorithm Fidanova S. Institute of Parallel Processing BAS 25A Acad. G. Bonchev Str., 1113 Sofia, Bulgaria Phone: +359 2 979 66 42 E-mail: stefka@parallel.bas.bg
More informationComplex Systems Made Simple
Erasmus Mundus Masters in Complex Systems Science Complex Systems Made Simple by Agent-Based Modeling and Simulation René Doursat http://iscpif.fr/~doursat Course Contents What this course is about (dense
More informationIntroduction to Artificial Life and Cellular Automata. Cellular Automata
Introduction to Artificial Life and Cellular Automata CS405 Cellular Automata A cellular automata is a family of simple, finite-state machines that exhibit interesting, emergent behaviors through their
More informationA Parallel Distributed Model of the Behaviour of Ant Colonies
J. theor. Biol. (1992) 156, 293-307 A arallel Distributed Model of the Behaviour of Ant Colonies DEBORAH M. GORDON'S, BRIAN C. GOODWIN~ AND L. E. H. TRAINOR Department of Biological Sciences, Stanford
More informationA Note on the Parameter of Evaporation in the Ant Colony Optimization Algorithm
International Mathematical Forum, Vol. 6, 2011, no. 34, 1655-1659 A Note on the Parameter of Evaporation in the Ant Colony Optimization Algorithm Prasanna Kumar Department of Mathematics Birla Institute
More informationSituation. The XPS project. PSO publication pattern. Problem. Aims. Areas
Situation The XPS project we are looking at a paradigm in its youth, full of potential and fertile with new ideas and new perspectives Researchers in many countries are experimenting with particle swarms
More informationCellular Automata: Statistical Models With Various Applications
Cellular Automata: Statistical Models With Various Applications Catherine Beauchemin, Department of Physics, University of Alberta April 22, 2002 Abstract This project offers an overview of the use of
More informationMetaheuristics and Local Search
Metaheuristics and Local Search 8000 Discrete optimization problems Variables x 1,..., x n. Variable domains D 1,..., D n, with D j Z. Constraints C 1,..., C m, with C i D 1 D n. Objective function f :
More informationDynamics and Chaos. Melanie Mitchell. Santa Fe Institute and Portland State University
Dynamics and Chaos Melanie Mitchell Santa Fe Institute and Portland State University Dynamical Systems Theory: The general study of how systems change over time Calculus Differential equations Discrete
More information