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Artificial Life in Virtual EnvironmentsArtificial Life in Virtual Environments

Rogério Perino de Oliveira NevesLaboratory of Integrable SystemsArtificial Life Group

A.L.I.V.E.A.L.I.V.E.

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Presentation PartsPresentation Parts

1. Introduction (3)

2. ALife Concepts (14)

3. Project Specification (19)

4. Experimental Results (12)

5. Conclusions (6)

Total(54)

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ObjectivesObjectives

Research on ALife Apply VR technologies to the visualization

of ALife experiments Build a customizable experimental

development framework Implement AL experiments within the

developed framework

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Project PartsProject Parts

Theoretical part– Related studies– “State of the Art” research look up– Open problems

Practical part– Framework development– Experiment development based on it

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About ALifeAbout ALife

Combines biology and computer science to create synthetic models of living systems evolution

A tentative to elucidate the logical structure (in a most general form) of biological evolution

Originally dominated by computer scientists Nowadays studied by researches of almost all areas

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Artificial LifeArtificial Life

The expression was first introduced by Christopher Langton in 1987, when was used as a conference name held inLos Alamos, New México, about “The Synthesis and Simulation of Living Systems”.

“Artificial life: The proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems” September, 1987, Los Alamos, New Mexico, Addison-Wesley Pub.

further information...

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Key conceptsKey concepts

The initial definition considered two types:– Strong AL: re-creation of life in-silico,

i.e. in the computer– Weak AL: simulation of biological

phenomenaPossible attacks

– Bottom-up– Top-down

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Bottom-Up– Observed in the nature– No planning– Comes from emergence/evolution– Generally associated to strong AL

Top-Down– Humanistic procedures– Comes from planning/foresight– Generally associated with weak AL

AttacksAttacks

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Life SimulationsLife Simulations

Rules– Local rather than global– Simple rather than complex– Emergent rather than pre-defined (behavior)

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Types of ALife researchTypes of ALife research

Origins of Life, self-organization and self-replication

Development and replication Evolutionary dynamics and adaptation Autonomous agents and robots Communication, cooperation and social behavior Simulation, synthesis tools and methodologies

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Open problems in Artificial LifeOpen problems in Artificial Life

A. How does life arise from the nonliving?

1. Generate a molecular proto-organism in vitro

2. Achieve the transition to life in an artificial chemistry in silico

3. Determine whether fundamentally novel living organizations

4. Simulate a unicellular organism over its entire lifecycle.

5. Explain how rules and symbols are generated from physical dynamics in living systems.

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Open problems in Artificial LifeOpen problems in Artificial Life

B. What are the potentials and limits of living systems?

6. Determine what is inevitable in the open-ended evolution of life.

7. Determine minimal conditions for evolutionary transitions from specific to generic response systems.

8. Create a formal framework for synthesizing dynamical hierarchies at all scales.

9. Determine the predictability of evolutionary consequences of manipulating organisms and ecosystems.

10. Develop a theory of information processing, information flow, and information generation for evolving systems.

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Open problems in Artificial LifeOpen problems in Artificial Life

C. How is life related to mind, machines, and culture?

11. Demonstrate the emergence of intelligence and mind in an artificial living system

12. Evaluate the influence of machines on the next major evolutionary transition of life

13. Provide a quantitative model of the interplay between cultural and biological evolution

14. Establish ethical principles for artificial life

further information...

From Bedau et. al – “Open Problems in Artificial Life”

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ALife toolsALife tools

Examples:

State Machines Non-linear Systems / Chaotic Dynamics Fuzzy Logic Artificial Neural Networks Evolutionary Search Genetic Algorithms

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Multi Agent SystemsMulti Agent Systems

Autonomous agents

Biological agents Robotic agents Computational Agents

... Artificialbiological

agents

Search agents

Entertainmentagents

Viruses Intelligentagents

ALife agentsElectronic agents

...

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Experiments in ALifeExperiments in ALife

Actor

Actor

Actor

Agent

Agent

Agent

Environment Virtual scene

UI / Interaction Visualization device

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Examples of ALife ProgramsExamples of ALife Programs

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State of the Art in ALifeState of the Art in ALife

* Neves, Rogério “Karl Sims videos”, http://www.lsi.usp.br/~rponeves/research/sims, access 18/09/2003

further information...

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PROJECT SPECIFICATION

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Project SpecificationProject Specification

Motivation Characteristics Resources Technical tools Java, Java3D Visualization / Interactivity Framework proposal Experiments

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MotivationMotivation Weak visualization and Interaction

– Most of ALife programs provides a poor visual representation of the Virtual Environment

– The programs allow only the change of some parameters at start-up or during the experiment

Hard code access– When available, the sources are frequently in low-level or at least not

object oriented languages (ASM, C) Support to parallel architectures

– Allowing the performance improvement in concurrent execution of agents in a Multi-threaded, Multi-Agent context

A full 3D Environment simulation– Allowing the employment of vector mathematics to operate objects

in the scene Apply the “State of the Art” in visualization technologies

– Employ computer graphics, accelerator boards and VR technologies to the visualization of the Virtual Environment

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Project featuresProject features

OOP Paradigm– Allows easy object/agent description/operation

Cross-platform execution capability Open source philosophy Simulation of a true 3D space with vector dynamics

– Providing easy manipulation of objects into 3D space Multiple-device 3D graphical support Visualization in Virtual Reality and immersive

environments Concurrent execution of programs

– Allow experiment speed-ups in multi-processed and distributed architectures

Browser applet / Internet execution

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Techniques / toolsTechniques / tools

OOP MAS Vector Mathematics Discrete Time-Dynamics Concurrent Programming Computer Graphics Networking ALife Related

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Development resourcesDevelopment resources

Java / Java3D API (from SUN) Personal Computers Graphical Workstations (Silicon Graphics) Multi-processed systems (SPADE project) Cluster of PCs (CAVE) Visualization devices (from monitors to CAVES) GB Ethernet Network

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Java & Java3DJava & Java3D

Java

Cross-platform capability Internet compliant Built over the OOP paradigm Concurrent programming support ( through Threads) Extensible Reliable

Java3D

New standard in VR development OpenGL/DirectX hi-level interface Scene description through scene-graphs Extend Java features

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Java3D scene-graph exampleJava3D scene-graph example

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VisualizationVisualization

Directed, but not limited to:

Ordinary 3D boards Professional graphical accelerators From ordinary to stereo Monitors Shutter Glasses Head Mounted Displays (HMD) CAVES Other VR devices

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InteractivityInteractivity

Mouse* Keyboard Gloves* Wands* Other tracking devices*

*Through Java3D picking behavior

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Levels of operabilityLevels of operability

A.L.I.V.E.Framework

Java/Java3D

Byte code

Super classes

Java VM/Machine Code

UserInterface

UserClasses

Mid-Level/Language Code

Hi-Level/Pseudo Code

Custom UserInterface

Runtime Interface/Interaction

Project Scope

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Platform architecturePlatform architecture

RenderClientSubset

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UML diagramUML diagram

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UML diagramUML diagram

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Render ClientRender Client

SceneMulticast Packages

Server

RenderClient RenderClient RenderClient

Env

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Environment configurationEnvironment configuration

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UIUI

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Agent diagram exampleAgent diagram example

SensoresAm biente

Cena

Pré-processam entodas variáveis de

entrada

Técnicas defiltragem e

processam entode sinais

Processo dedecisão

Associaçãocruzada,

Redes neurais,Com putação

fuzzy,etc.

Variáveis desaída

AtivadoresContro le

AçãoM ovim entação

Interação

Acionadoresm ecânicos,Funções de

Reprodução,A lgorítm osgenéticos

Variáveis locais

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Demo codeDemo code

DEMO CODE

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EXPERIMENTS & RESULTS

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Developed ExperimentsDeveloped Experiments

Program test ALGA – Evolution / Adaptation Predator-Prey system Fish Schooling Flocking Biological demos

– Cellular dynamics– Fungus growth– Lymphocytes & virus– Mitosis

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Demo ExperimentsDemo Experiments

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Predator-Prey SystemPredator-Prey System

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Predator sightPredator sight

R

G

B

+ ACT

W1

W2

W3

FILTER

RADIATION

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Predator DNAPredator DNA Reproduction condition Death condition Sensibility radius Strength Stamina Metabolism temperature Temperature tolerance Toxic resistance W1: red filter weight (R) W2: green filter weight (G) W3: blue filter weight (B)

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Predator-Prey population graphsPredator-Prey population graphs

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Predator-Prey population graphsPredator-Prey population graphs

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Visual improvementsVisual improvements

Cellular dynamics

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Fish SchoolingFish Schooling

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FlockingFlocking

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Performance Performance -1-1

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ConclusionsConclusions

The project explores the representational potential of ALife experiments employing 3D and VR to the visualization of experiment environment

The developed framework provides a quick experiment prototype development tool

The developed experiments demonstrates the framework capabilities and resources, serving as models to new user experiments

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ConclusionsConclusions

Making the project available in Sourceforje.net, users around the world are allowed to contribute to the framework improvement

The developed experiments can be published thought the internet allowing greater and faster interaction between research groups

Also allows ordinary people outside the scientific community to experiment with this experimental virtual lab, serving as a scientific divulgation tool

The experiments can take advantage of new coming visualization technologies while they appear, without the need of code adaptation

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Possible employmentsPossible employments

ALife experiment development Biological scholar demonstrations Problem solving in sciences/engineering * System training in robotics * Simulation of genetics and evolutionary systems User oriented pattern search in virtual spaces Employment in future technologies (such

nanotechnologies)

* Neves, Rogério P. O. and Netto, Marcio L.

“Evolutionary Search for Optimization of Fuzzy Logic Controllers”

1st International Conference on Fuzzy Systems and Knowledge Discovery, Volume I, on Hybrid Systems and Applications I

further information...

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Proposal to future worksProposal to future works

Interaction through sensitive devices File access Experiments:

– Variable morphology– Intelligent agents / humanoids

* Cavalhieri, Marcos, “Virtual Human Project”, http://www.lsi.usp.br/~mac/ , access in 18/09/2003

further information...

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AcknowledgmentsAcknowledgments

Claudio Ranieri , Group ARTLIFE, LSI, USP Marcos Cavalhieri, Group ARTLIFE, LSI, USP Prof. Emilio Hernandez, LSI, USP Artur Gonzalez, PCS, USP Prof. Wolfgang Banzhaf, Dortmund University

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Related DocumentsRelated Documents

Rogério Neves, ALIVE Project Site & thesis

http://www.lsi.usp.br/~rponeves/

Official ALIVE Project site

http://www.lsi.usp.br/~alive/

ARTLIFE Site, Artificial Life group

http://www.lsi.usp.br/~artlife/

Questions & doubts

rponeves@lsi.usp.br

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