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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
11
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
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
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, …
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 ?
Why ?
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, …
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