The Swarm The Swarm Simulation System Simulation System

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The Swarm The Swarm

Simulation System Simulation System

Je planmäßiger die Menschen vorgehen,

desto wirksamer vermag sie der Zufall treffen .

Katja Luther, Holger Meyer

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From Elements to Systems - The Swarm Simulation System 2

Outline

 Why is the influence of interventions on complex systems (ecosystems, economic systems,

metabolism) usually not predictable?

 Criteria for simulation systems

- Representation of complex interactions

- Agentbased model

- Building of hierarchical structures (recursion)

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Why Simulation?

 John Briggs, F. David Peat: "Komplexe Systeme sind letzen Endes nicht

analysierbar, nicht auf Teile reduzierbar, weil die Teile durch Annäherung und

Rückkopplung ständig aufeinander

einwirken".

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Scenarios

 Simulation of ecosystems

 „Superorganisms“ (e.g. ants, bees, etc.)

 Processes in business companies (micro) and national economy (macro)

 Load balancing in telecommunication

networks

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The Swarm Simulations System

 Basic principle: groups of simple interacting agents creating a complex system behavior

 Swarm provides a general for writing computer simulations

 The Swarm system supports implementation of

multi-level systems by composing hierarchical

swarms of agents

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What is Swarm?

A virtual machine / a discrete event simulator

 Virtual machine: an abstract instruction processor.

Example: the Java virtual machine

 But instead of Java instructions, Swarm interprets instructions describing temporal events and

constraints via Schedules, Swarms, and their

associated parameterizations

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Swarm as a virtual computer

Operating System Swarm kernel

GUI Model

CPU

 A computer’s CPU executes program instructions

 Swarm kernel is virtual CPU running model

and GUI events

 Nested Swarms merge

activity schedules into

one

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Swarm System Design

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Real World Multi-level Systems

 Global Economy

 National Economy

 Business Company

 Employee

 Ecosystem

 Ant Colony or Beehive

 An Ant or Bee

 Organ

 Cell

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Hierarchical Modeling by

Swarm

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Interactions of Nested Swarms

 Swarm allows swarms to be agents creating a new

swarm in the next level of the hierarchical model

(nested swarms)

 Time hierarchy:

Top-down synchronized

time-management across the

simulation

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Example: Heatbugs

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Implementation Heatbugs

public class Heatbug {

/** my current unhappiness */

public double unhappiness;

/** my spatial coordinates */

public int x, y;

/** my ideal temperature and how much heat I put out */

public int idealTemperature, outputHeat;

/** chance of moving randomly */

public double randomMoveProbability;

/** my colour (display) */

public byte bugColor;

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Bottom up modeling

Organizations of agents Animate agents

Data Observer Inanimate agents

If

<cond>

then

<action1>

else

<action2>

If

<cond>

then

<action1>

else

<action2>

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Implementation ObserverSwarm

public class HeatbugObserverSwarm extends GUISwarmImpl { /** one parameter: update freq */

public int displayFrequency;

/** ActionGroup for sequence of GUI events */

public ActionGroup displayActions;

/** the single Schedule instance */

public Schedule displaySchedule;

/** the Swarm we're observing */

public HeatbugModelSwarm heatbugModelSwarm;

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HeatbugsModelSwarm

RepeatIntervall=1 stepRule step upateLattice

ActionGroup

heatspace type=heatspace

world type=Grid2d

environment

count=100 class

heatbug

populate

agents

Heatbug

idealTemperature(1700<=uniformRandom<=3500) outputHeat(3000<=uniformRandom<=10000)

imgColor(byteConstant=64)

classes

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Implementation Modelswarm

public class HeatbugModelSwarm extends SwarmImpl{

// simulation parameters public int numBugs;

public int maxOutputHeat, minOutputHeat;

public double evaporationRate;

public double diffuseConstant;

public int worldXSize,worldYSize;

public int minIdealTemp, maxIdealTemp;

public double randomMoveProbability;

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Implementation Modelswarm-2

public class HeatbugModelSwarm extends SwarmImpl{

...

/** ActionGroup for holding an ordered sequence of action

*/

public ActionGroup modelActions;

/** the single Schedule */

public Schedule modelSchedule;

/** list of all the heatbugs and the world the bugs live in

*/

public List heatbugList;

public Grid2d world;

public HeatSpace heat;

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Probes and GUI

Executes method

Input value for variable Open probe for class Close probe

Probe

 A probe can be used to communicate with the objects in real

time

 Default probe map

shows all variables

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Implementation ProbeMap

//in contructor of HeatbugModelSwarm

class HeatbugModelProbeMap extends EmptyProbeMapImpl {

… //definition of addVar() and addMessage()

public HeatbugModelProbeMap (Zone _aZone, Class aClass) { super (_aZone, aClass);

addVar ("numBugs");

…

addMessage ("toggleRandomizedOrder");

addMessage ("addHeatbug:");

} }

/*Now, install our custom probeMap class directly into the probeLibrary */

...

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Method buildObjects()

public Object buildObjects (){

super.buildObjects();

heat = new HeatSpace(getZone(),worldXSize, worldYSize,diffuseConst, evapoRate);

world = new Grid2dImpl (getZone(), worldXSize, worldYSize);

heatbugList = new LinkedList();

for (i = 0; i < numBugs; i++) {

idealTemp = getRandBetween(minIdealTemp, maxIdealTemp);

outputHeat = getRandBetween(minOutputHeat, maxOutputHeat);

Heatbug hbug = new Heatbug (world, heat);

heatbugList.add (hbug);

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Implementation buildActions()

public Object buildActions () { super.buildActions();

modelActions = new ActionGroupImpl (getZone());

//erzeugen der Actionsund zufügen zu ActionGroup modelActions.createActionTo$message

(heat, new Selector (heat.getClass (),

“updateLattice", false));

//erzeugen von modelSchedule und zufügen der ActionGroup modelSchedule = new ScheduleImpl (getZone (), 1);

modelSchedule.at$createAction (0, modelActions);

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Arborgames

Model of forest dynamics

 Examine the role of fire on species diversity

 Discrete cells with one (two) individual per cell

 Local interaction of trees in a neighborhood

generates landscape

dynamics

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Arborgames

Response to disturbance

 Landscape dynamics allow recursive

response to disturbance

 Expansion of fire is

governed by the

contagion of local

forest structure

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Simulation of population dynamics

 Development of

population diversity in a forest with medium

strength of disturbance

 Development of

population diversity with

strong disturbance

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And now something

completely different ...

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Computational-Fields

 Movement of the agents is driven by these Co-Fields

 Co-Fields are established by the infrastructure and by the agents

 Agent movements induces changes of Co-Field –

composing a feedback cycle

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The Swarm API



objectbase Base classes for simulated objects – the

"agents“



activity Schedule execution mechanism – the

"process"



space Spatial environment library



simtools Collected tools (non-GUI) useful for developing Swarm simulations



gui Graphical Interface library



analysis Objects that help with data processing

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Using Swarm API: Museum

import swarm.Globals;

import swarm.space.Discrete2dImpl;

public class Museum extends Discrete2dImpl { public ListImpl rooms;

...

// In Constructor:

rooms = new ListImpl(Globals.env.globalZone);

...

public void draw(){

((Room)rooms.atOffset(0)).draw(startX,startY);

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Using Swarm API: Agents

import swarm.objectbase.Swarm;

import swarm.objectbase.SwarmImpl;

import swarm.activity.ActionGroupImpl;

import swarm.activity.ScheduleImpl;

import swarm.activity.Activity;

public class PeopleSwarm extends SwarmImpl{

...

public Activity activateIn(Swarm swarmContext) { super.activateIn(swarmContext);

peopleSchedule.activateIn(this);

return getActivity();

}

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Using Swarm API: GUI

public void createFieldRasters() { ZoomRasterImpl fieldRaster;

int numFields = modelSwarm.museumSwarm.museum.rooms.getCount();

fieldRasters = new ListImpl(getZone());

for(int i=0; i<numFields;i++) {

fieldRaster = new ZoomRasterImpl(getZone(),"room

"+i+“fieldRaster");

fieldRaster.setWidth$Height(museum.getSizeX(), museum.getSizeY());

fieldRaster.setWindowTitle("room ;"+i+" field");

fieldRasters.addLast(fieldRaster);

...

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Resources



www.swarm.org



Chris Langton et. al.: The Swarm Simulation System A tool for studying complex systems, Santa Fe Institute 1999



www.ctsgroup.ch



The Co-Fields Project – Agent Group (Franco Zamonelli) Università di Modena e Reggio Emilia

polaris.ing.unimo.it/didattica/curriculum/marco/Web-Co- Fields/cofields.html



Swarm User Guide, Swarm Development Group, University of Kansas Department of Political Science, Paul Johnson, 2000

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