in vivo veritas: towards the evolution of...

In Vivo Veritas:towards the Evolution of Things

Gusz EibenVU University Amsterdam, NL

University of York, UK

PPSN XIII, Ljubljana, 16-9-2014

I have a dream

Programmableevolutionary systems(evolutionary computing)

Real-world evolutionary systems(biosphere)

Evolutionof Things

20th century (r)evolution

WETWARE

EVOLUTION

Biosphere

In vivo

Evolutionary Computing

in silico

SOFTWARE

Fundamental insight

Individuals

Selection

Reproduction

Evolutionary Algorithm

Evaluation

Variation

Selection

Initialization

Termination

Macroscopic view (after Dennett)

If you have variation, heredity, and selection, then you must get evolution.

Cheats

Fitness (a priori)

Execution (centralized)

Genotype-phenotype mapping (simple)

Population (size, structure)

To build an EA we need

Phenotypes (solution)

Genotypes (code) *

Variation operators

Fitness measure

Selection operators

* A.k.a. representation

• Increasing diversity by variation– mutation– recombination

Push towards NOVELTY

• Decreasing diversity by selection– of parents– of survivors

Push towards QUALITY

Balance is essentialRegulated by the EA parameters

The two main forces behind EAs

When the going gets tough the EAs get going

An EA is the 2nd best solver for any problem

21st century (r)evolution

WETWARE HARDWARE

EVOLUTION

Evolution of Things

in materio

SOFTWARE

Evolutionary Computing

in silico

EVOLUTION

Biosphere

in vivo

Co-evolving bodies and minds

• Simple virtual creatures with evolvable bodies and minds• Body: always evolved, mind: 1) evolved or 2) learned• 1) and 2) lead to different BODIES !• HOW REAL IS THIS ?!

Buresch, Eiben, Nitschke, SchutEffects of Evolutionary and Lifetime Learning on Minds and Bodiesin an Artifical SocietyCEC 2005

Robots ?

Animate artefact = robot

Off-line vs. on-line

Design stage Operational stage

Time flow

D

Off-line evolution On-line evolution

Evaluation

Variation

Selection

Initialization

Termination

genotype

fitness

BLACK BOX1110001010

f = 3.14

EAs, robots, simulators

Funes, Pollack, Evolutionary Body Building, Artificial Life 4(4), 1998

• EA + simulator • Evolves 2D and 3D LEGO objects• Built in real world afterwards

• Inanimate (mindless) individuals• Off-line evolution • Evolution in simulation (no

hardware in the loop)

Lipson, Pollack, Automatic design and manufacture of robotic lifeforms, Nature 406, 2000

• GOLEM project• EA + simulator• Co-evolution of body + controller• Evolved robot fabricated

afterwards “hands-free”

• Off-line evolution• Evolution in simulation

Watson, Ficici, Pollack, Embodied Evolution,Robotics and Autonomous Systems 39, 2002

• Physical robot population• Distributed on-line EA for evolving

controllers

• Bodies are fixed

Lund, Co-evolving Control and Morphology with LEGO Robots, in Morpho-functional Machines, Springer, 2003

• LEGO robots• Co-evolution of body + controller• Body space: 825 possible

configurations• Controller space: 6 weights of

connections

• Small search space• Off-line evolution• Evolution in simulation

Zykov, Mytilinaios, Adams, Lipson, Self-reproducing machines, Nature 435, 2005

• Physical system with Molecubes• Raw material manually replenished• Robot constructs a replica by lift-

ing and assembling cubes from the feeding locations

• Not evolvable (no genotype)• 2nd robot is exact clone• “Self…” is a bad idea, cf ethics later

Zykov, Mytilinaios, Desnoyer, Lipson, Evolved and Designed Self-Reproducing Modular Robotics, IEEE Tr. on Robotics 23(2), 2007

• Fitness = ability to self-replicate• 1) Evolve morphologies for going to

place with “spare parts” • 2) Evolve controllers for assembling

“spare parts”• Simulated - two machines are

physically built

• 2nd robot is exact clone• “Self…” is a bad idea, cf ethics later

The SYMBRION project2008-2013

• Modular robot organisms with reconfigurable morphologies

• Modules are autonomous robots –move & (dis)aggregate themselves

• Online evolution of controllers on the modules

• Organisms do not reproduce(transient, not permanent)

• Little work on real hardware

Bredeche, Montanier, Liu, Winfield, Environment-driven Distributed Evolutionary Adaptation in a Population of Autonomous Robotic Agents,

MCMDS 18(8), 2011

• Physical robot population (e-pucks)• Distributed on-line EA for evolving

controllers (meet = mate)• No explicit fitness function

• Bodies are fixed

John Long, Darwin's Devices: What Evolving Robots Can Teach Us About the History of Life and the Future of Technology, Basic Books 2012

• Studied a biological question• Real robots form physical model of

a biological phenomenon• All fitness evaluations are in

hardware • It really teaches us something (no

spoiler here)

• It takes LONG (sic!)

Miller, Harding, Tufte, Evolution-in-materio: evolving computation in materials, Evolutionary Intelligence 7(1), 2014

• Evolving computation using materials in physical or chemical systems

• Physical system does computation• EA runs on a PC• EA provides configuration signals

for physical system

• Construction of (animate) artefactsis not an issue

Evolutionary Robotics

Controllers Morphologies

Off-line

On-line

Embodiment

Result in

Digital space Physical space

Evolution in

Digital space Evolutionaryoptimization

Evolutionarydesign /robotics

Physical space DNA computing?Evolution of

Things

What is missing?

Diagram List

Evaluation

Variation

Selection

Initialization

Termination

Phenotypes (solution)

Genotypes (code) *

Variation operators

Fitness measure

Selection operators

Diagram List

Survival, mating, (task exec.)1

2

3

Delivery

Conception Fertility

Artifacts (animate)

Genetic encoding

Real birth (+ death)

Fitness (?)

Selection

The Triangle of Life

• ToL = a generic framework with many possible “incarnations”• Decomposes universal life cycle into 3 stages• Provides guidelines for implementation• Defines a class of artificial life systems

Eiben, Bredeche, Hoogendoorn, Stadner, Timmis, Tyrrel, WinfieldThe Triangle of Life: Evolving Robots in Real-time and Real-spaceECAL 2013

Survival, mating, (task exec.)1

2

3

Delivery

Conception Fertility

The Triangle of Life

What is really missing?

“Birth” for artefactsreproduction / heredity

Ethics

KILL SWITCH

Don’t want

In vivo veritas = It is for real

Novel Artificial Evolution:

• No “oracle” • Search space has no-go areas• Fast progress needed • Population is structured (space / time)• EA must be self-calibrating• Environmental selection and noise are given

Research M.O.• Choose substrate, specify artefacts• Define inheritable elements (genotypes for

morphology and controller features)• Define birth procedures• Specify selection mechanisms (task-based?)

• Integrate (ToL, learning, observation)• Design habitat (w/ or w/o tasks)• Set goals, make hypotheses, run experiments, …

Some pieces

1. Autonomous (de-)selection2. Self-adaptive fitness evaluation

times3. Combination of environmental

and task-based fitness4. First full proof of concept

Wickramasighe, van Steen, Eiben, Peer-to-peer EAs with Adaptive Autonomous Selection, GECCO 2007

• Selection probabilities by a sigmoid function with two parameters m and s

• Each individual – has its own m and s that change over time– mates and dies when it feels like it

• Population size = ?• Outcomes:

– Problems get solved– Population sizes remain within bounds

Dinu, Dimitrov, Weel, Eiben, Self-adapting Fitness Evaluation Timesfor On-line Evolution of Simulated Robots, GECCO 2013

• Controllers evolved in a robot swarm• Evaluation time τ

– Short noise– Long too few evaluations

• τ is genetically encoded and self-adapted, co-evolves with controllers

Self-adapting fitness evaluation times cont’d

Situation A Situation CSituation B

Situation A – B – C Situation B – A – C

Haasdijk, Bredeche, Eiben, Combining Environment-Driven Adaptation and Task-Driven Optimisation in Evol. Robotics, PLOS One 9(6) 2014

• Controllers evolved in robot swarm• MAIN IDEA:

– Parent selection by task performance– Survivor selection by environment

• Validation by quantifying & measuring viability and performance

# of inseminations

Viability

# of pucks collected

Task performance

Random trend vs. driven trend

Environment-driven & task-driven evolution

Weel, Crosato, Heinerman, Haasdijk, Eiben, A Robotic Ecosystem with Evolvable Minds and Bodies, IEEE SSCI 2014, to appear

• First proof of concept• Implements the whole Triangle of

Life in an ecosystem• Organisms are made from Roombots• On-line gait learning during Infancy• No tasks during adult life• Meeting = mating

A robotic ecosystem… cont’d

Birth Clinic

15 m

• Constructible: existing robots, hi-fi simulator

• It works• Battery

A robotic ecosystem cont’d

Why ?

Fundamental studiesof evolution in new substrates (generalization)

Evolutionof Things

Evolutionary engineeringof animate artifacts

Why ?

Fundamental studiesof evolution in new substrates (generalization)

Evolutionof Things

Evolutionary engineeringof animate artifacts

MAKE UNDERSTAND

Why ?

Applications – new kind of robotics

Science – new kind of biology, ALife, AI

Evolutionary Computing 2.0

Robotics

Medical nano-robots, personal virus scanners, …

Artificial pets, deep mine workers, sterraforming, …

ScienceFor the first time: co-evolvable body and mind

• Morphological computing, Pfeifer & Bongard 2006– “how the body shapes the mind” … …– Evolvability, morph vs controller complexity, …

• The evolution structure, control, function• Conditions for evolution• The origin of species, Cambrian explosion• …

Looking even further …

• New hardware• New software• New program, …

Evol. Computing Evolution of Things

Changes• Science:

• Bio-tech tech bio

• new field based on ALife + Robotics + Evol Comp

• Industry:

• new robotics: production reproduction

• Internet of Things meets the Evolution of Things

• Society:

• New economy (in some sectors)

• Ethical and legal issues

You are here

Additional info

PAPERS: • Evolutionary Intelligence journal, dec 2012• Frontiers Journal in Robotics and AI, june 2014

VIDEO: TEDx talk“Tech Kangaroos”youtube / eiben

in vivo veritas: towards the evolution of...

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