1. The document discusses various examples of complex systems, including physical pattern formation in liquids, biological pattern formation in animal coats, neural networks in the brain, swarm intelligence in insect colonies, and collective motion in herds and schools.
2. Complex systems are characterized by a large number of interacting agents following simple local rules that lead to emergent complex behavior at the global scale without centralized control.
3. The examples presented illustrate how decentralized interactions between many individual parts or agents can self-organize into complex coherent structures and patterns through basic principles like feedback, synchronization, and reinforcement.
This document provides an introduction to complex systems and agent-based modeling. It discusses what complex systems are, including examples ranging from simple systems of a few agents to more sophisticated systems involving many agents. Complex systems are characterized as having emergent behaviors that arise from the interactions of the agents following simple rules, without any centralized control. The document also provides examples of complex systems in nature, such as pattern formation, neural networks, swarm intelligence in insect colonies, collective motion of flocking and schooling, and social biological systems.
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This document discusses the concept of morphogenetic engineering, which aims to design artificial self-organized systems capable of developing elaborate architectures without central planning. It begins by looking at natural complex systems like animal flocking and termite mounds that self-organize. The focus is on "architectures without architects" in biological systems. Morphogenetic engineering is proposed as a new type of engineering that designs self-organizing agents, not the architectures directly, taking inspiration from embryogenesis, simulated development and synthetic biology. Several research projects are summarized that aim to model biological development and create modular, programmable artificial self-construction.
This document discusses morphogenetic engineering, which aims to design decentralized systems capable of developing elaborate morphologies without central planning. It covers three main topics:
1) Engineering and control of self-organization, which involves fostering and guiding complex systems through their elements.
2) Morphogenetic engineering, which explores artificial design of systems that can develop architectures like those seen in biology, with heterogeneous and hierarchical structures emerging from self-organization.
3) Embryomorphic engineering, which takes inspiration from biological morphogenesis and development, aiming to design multi-agent models that can undergo evolution and development like living organisms. The goal is to better understand novelty in evolution by studying emergence at the microscopic, agent level.
The document discusses complexity, self-organized criticality, and their relationship to freedom. It argues that freedom is greatest within complex networks that have organized themselves into a critical state between order and randomness. In such a state, small actions can have large effects, increasing the potential impact and value of individual choices. Thus the complexity of surrounding networks influences the degree of freedom available.
International journal of engineering issues vol 2015 - no 1 - paper3sophiabelthome
This document summarizes a paper on modeling evolving complex software systems as cyber-physical systems using principles from physics and mathematics. It discusses how software systems can be viewed as complex automatons with mathematical foundations in areas like complex numbers and Fourier transforms. Cybernetics provides tools to model human behaviors and interactions in these systems. The paper also discusses how analog computers were early models of physical phenomena, and how infinitesimals and differentials from calculus can model continuously changing aspects of cyber-physical systems, within the limits imposed by physical reality.
Technology at the angstrom level, and the future of nanotechnology. Introduces the EMI diagram (Energy, Mass, and Information) of angstrom engineering.
This document provides an introduction to complex systems and agent-based modeling. It discusses what complex systems are, including examples ranging from simple systems of a few agents to more sophisticated systems involving many agents. Complex systems are characterized as having emergent behaviors that arise from the interactions of the agents following simple rules, without any centralized control. The document also provides examples of complex systems in nature, such as pattern formation, neural networks, swarm intelligence in insect colonies, collective motion of flocking and schooling, and social biological systems.
This document summarizes several agent-based modeling projects done by students at the University of East London. It describes projects using StarLogo, where students modeled emergent urban forms and traffic patterns. It also discusses modeling the growth of traditional Yemeni cities and experiments deforming NURBS surfaces using agent-based modeling in Microstation. The document provides examples of how agent-based modeling can be used as a design tool to explore emergent patterns and behaviors.
This document discusses the concept of morphogenetic engineering, which aims to design artificial self-organized systems capable of developing elaborate architectures without central planning. It begins by looking at natural complex systems like animal flocking and termite mounds that self-organize. The focus is on "architectures without architects" in biological systems. Morphogenetic engineering is proposed as a new type of engineering that designs self-organizing agents, not the architectures directly, taking inspiration from embryogenesis, simulated development and synthetic biology. Several research projects are summarized that aim to model biological development and create modular, programmable artificial self-construction.
This document discusses morphogenetic engineering, which aims to design decentralized systems capable of developing elaborate morphologies without central planning. It covers three main topics:
1) Engineering and control of self-organization, which involves fostering and guiding complex systems through their elements.
2) Morphogenetic engineering, which explores artificial design of systems that can develop architectures like those seen in biology, with heterogeneous and hierarchical structures emerging from self-organization.
3) Embryomorphic engineering, which takes inspiration from biological morphogenesis and development, aiming to design multi-agent models that can undergo evolution and development like living organisms. The goal is to better understand novelty in evolution by studying emergence at the microscopic, agent level.
The document discusses complexity, self-organized criticality, and their relationship to freedom. It argues that freedom is greatest within complex networks that have organized themselves into a critical state between order and randomness. In such a state, small actions can have large effects, increasing the potential impact and value of individual choices. Thus the complexity of surrounding networks influences the degree of freedom available.
International journal of engineering issues vol 2015 - no 1 - paper3sophiabelthome
This document summarizes a paper on modeling evolving complex software systems as cyber-physical systems using principles from physics and mathematics. It discusses how software systems can be viewed as complex automatons with mathematical foundations in areas like complex numbers and Fourier transforms. Cybernetics provides tools to model human behaviors and interactions in these systems. The paper also discusses how analog computers were early models of physical phenomena, and how infinitesimals and differentials from calculus can model continuously changing aspects of cyber-physical systems, within the limits imposed by physical reality.
Technology at the angstrom level, and the future of nanotechnology. Introduces the EMI diagram (Energy, Mass, and Information) of angstrom engineering.
The document discusses modelling large complex systems using multi-agent technology. It describes complex systems as consisting of many interdependent and autonomous components that exhibit emergent and unpredictable behavior from nonlinear interactions. The only technology capable of accurately modelling complex systems is said to be multi-agent systems, where software agents interact and self-organize to produce intelligent emergent behavior. Several examples are given of multi-agent models being used successfully in commercial applications.
Shape of Sound is Victoria Meyers' new book. The PowerPoint presentation is Meyers' presentation from sxsw.Eco, where Meyers was an invited presenter. Meyers has a reputation for studying and applying forms of energy to architecture and architectural design. This includes light, sound, and wind. Meyers's most recent book, Shape of Sound, is published May 1, 2014, by Artifice Books, London
The document traces the history and development of the concept of a system. It discusses how Nicolas Léonard Sadi Carnot first studied systems in steam engines in 1824. Rudolf Clausius later generalized this in 1850 to include the concept of surroundings. Significant further development was done by biologists, mathematicians, and interdisciplinary researchers in the 20th century. The document then provides definitions of a system as a set of interconnected parts working together toward a common goal and maintains homeostasis. It also lists the typical elements of a system.
This document discusses common features of complex systems and networks. It notes that complex systems generally have a large number of elements that follow individual behavior rules and interact locally. The systems exhibit node and link diversity and dynamics. They can display hierarchy across different levels and heterogeneity. Complex networks form the backbone of complex systems. Network structure influences function and vice versa. Three key metrics to characterize networks are described - average path length, degree distribution, and clustering coefficient. Different types of networks, including random, regular, small-world and scale-free are also discussed.
This document discusses complexity science and complex adaptive systems. It defines complexity and distinguishes between simple, complicated, and complex systems. Complex systems have many interrelated and autonomous parts that interact in non-linear ways, making their behavior hard to predict. The document introduces the Cynefin framework for categorizing systems and describes properties of complex adaptive systems, including emergence and self-organization. It emphasizes that the whole of a complex system is greater than the sum of its parts and advocates developing a complexity mindset to understand and leverage complexity.
Complexity Número especial da Nature Physics Insight sobre complexidadeaugustodefranco .
Albert-László Barabási, James P. Crutchfield, M. E. J. Newman, Alessandro Vespignani, Jianxi Gao, Sergey V. Buldyrev, Eugene Stanley and Shlomo Havlin Janeiro 2012
NG2S: A Study of Pro-Environmental Tipping Point via ABMsKan Yuenyong
A study of tipping point: much less is known about the most efficient ways to reach such transitions or how self-reinforcing systemic transformations might be instigated through policy. We employ an agent-based model to study the emergence of social tipping points through various feedback loops that have been previously identified to constitute an ecological approach to human behavior. Our model suggests that even a linear introduction of pro-environmental affordances (action opportunities) to a social system can have non-linear positive effects on the emergence of collective pro-environmental behavior patterns.
1. The study of chaos analyzes nonlinear dynamical systems that are highly sensitive to initial conditions. While a universal definition of chaos is still lacking, mathematicians generally agree that chaos involves sensitive dependence on initial conditions, mixing, and dense periodic points.
2. This paper formulates a new approach to studying chaos in discrete dynamical systems based on concepts from inverse problems, set-valued mappings, graphical convergence theory, and topology. The author argues that order, chaos and complexity can be viewed as parts of a unified mathematical structure applying topological convergence theory to increasingly nonlinear mappings.
3. By applying concepts from spectral approximation theory and introducing "latent chaotic states", the author aims to develop a theory of chaos and interpret how nature
The document proposes creating an interactive simulated environment where data serves as energy, objects grow and die based on data flow, and the user is part of the ecosystem affected by it and able to affect it. The environment would use tools like brain-computer interfaces to provide biochemical feedback to the user. The goal is to explore new types of human-environment interaction beyond passive observation by translating natural laws to this new context.
Bruce Damer's talk at EE380, the Stanford University Computer Systems Colloqu...Bruce Damer
The document discusses the EvoGrid, a worldwide computational effort to simulate the chemical origins of life on Earth. The EvoGrid uses a large network of computers to simulate a primordial soup and model the emergence of increasingly complex structures and reaction sequences from simple starting conditions. The goal is to gain insights into how life may have first emerged on our planet through a bottom-up, chemistry-first approach rather than assuming the prior existence of biological functions or mechanisms.
This work argues that the emerging understanding of time in quantum information science can be articulated as a philosophical theory of change. Change and time are interrelated, and one can be used to interrogate the other, namely, a theory of change can be derived from a theory of time. What is new in quantum science is time being regarded as just another property to be engineered. At the quantum scale, time is reversible in certain ways, which is quite different from the everyday experience of time whose unidirectional arrow does not allow a dropped egg to reassemble. At the quantum scale of atoms, though, a particle retains the history of its trajectory, which may be retraced before collapsed in measurement.
Quantum scientists evolve systems backward and forward in time, controlling phase transitions with Floquet engineering. Quantum systems are entangled in time and space, with temporal correlations exhibiting greater multiplicity than spatial correlations. The chaotic time regimes of ballistic spread followed by saturation are implemented in quantum walks for faster search and heightened cryptosecurity. In quantum neuroscience, seizure may be explained by chaotic dynamics and normal resting state by Floquet-like periodic cycles. Time is revealed to have the same kinds of repeating structures as space (described by entanglement, symmetry, and topology), differently instantiated and controlled.
The quantum understanding of time can be propelled into a macroscale-theory of change through its connotation of a more flexible, malleable, probabilistic interface with reality. Change becomes less rigid. Probability is the lever of change, but notoriously difficult for humans to grasp, as we think better in storylines than statistics. The idea of manipulating quantum system properties in which time, space, dynamics (change), are all just parameters, is an empowering frame for the acceptance of change. The quantum mindset affords greater facility with probability-driven events (change).
Miranda p 2000: swarm modelling_the use of swarm intelligence to generate arc...ArchiLab 7
This document summarizes a paper about using swarm intelligence to generate architectural form. It describes how simple agents or "turtles" can exhibit complex emergent behaviors through their interactions with environments and each other. The paper explores how swarms can structurally couple with their environment to implicitly describe and recognize different shapes and forms, similar to how human perception works. It then discusses using swarm trajectories to construct 3D flocking simulations for exploring architectural design spaces.
OKFN Greece meet-up
Friday, April 6, 2012, 5:00 PM
Aristotle University of Thessaloniki, Research Dissemination Center
Prof. I. Antoniou (Director of MSc Web Science, AUTH, Steering Committee OKFN Greece). The power of Openness. Open Data and Open Knowledge
This document discusses various approaches to natural computing, including artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, molecular computing, and quantum computing. It also discusses several nature-inspired models of computation such as cellular automata, neural networks, evolutionary computation, swarm intelligence, artificial immune systems, and amorphous computing. Specific examples discussed include the Game of Life cellular automaton, ant colony optimization, artificial immune systems, amorphous computing, and artificial life simulations.
Linguistics models for system analysis- Chuluundorj.BKhulan Jugder
This document discusses various models and concepts for analyzing systems, including:
1. It describes different types of systems including mechanistic, animate, social, and ecological systems.
2. It discusses open, closed, and semi-closed systems and how they interact with their environments.
3. Several mathematical and scientific concepts are proposed as models for linguistic and semantic analysis, such as group theory, topology, Hilbert spaces, and quantum mechanics.
4. The document suggests that these concepts from mathematics, physics, and other fields can provide frameworks for understanding semantic structures, mental representations, and cognitive processes.
Mr. Brainwash ❤️ Beautiful Girl _ FRANK FLUEGEL GALERIE.pdfFrank Fluegel
Mr. Brainwash Beautiful Girl / Mixed Media / signed / Unique
Year: 2023
Format: 96,5 x 127 cm / 37.8 x 50 inch
Material: Fine Art Paper with hand-torn edges.
Method: Mixed Media, Stencil, Spray Paint.
Edition: Unique
Other: handsigned by Mr. Brainwash front and verso.
Beautiful Girl by Mr. Brainwash is a mixed media artwork on paper done in 2023. It is unique and of course signed by Mr. Brainwash. The picture is a tribute to his own most successful work of art, the Balloon Girl. In this new creation, however, the theme of the little girl is slightly modified.
In Mr. Brainwash’s mixed media artwork titled “Beautiful Girl,” we are presented with a captivating depiction of a little girl adorned in a summer dress, with two playful pigtails framing her face. The artwork exudes a sense of innocence and whimsy, as the girl is shown in a dreamy state, lifting one end of her skirt and looking down as if she were about to dance. Through the use of mixed media, Mr. Brainwash skillfully combines different artistic elements to create a visually striking composition. The vibrant colors and bold brushstrokes bring the artwork to life, evoking a sense of joy and happiness. The attention to detail in the girl’s expression and body language adds depth and character to the piece, allowing viewers to connect with the young protagonist on a personal and emotional level. “Beautiful Girl” is a testament to Mr. Brainwash’s unique artistic style, blending elements of street art, pop art, and contemporary art to create a visually captivating and emotionally resonant artwork.
The use of mixed media in “Beautiful Girl” adds an additional layer of complexity to the artwork. By combining different artistic techniques and materials, such as stencils, spray paint, and collage, Mr. Brainwash creates a dynamic and textured composition that grabs the viewer’s attention. The juxtaposition of different textures and patterns adds depth and visual interest to the piece, while also emphasizing the artist’s eclectic and experimental approach to art-making. The inclusion of collage elements, such as newspaper clippings and torn posters, further enhances the artwork’s urban and contemporary feel. Overall, “Beautiful Girl” is a visually captivating and thought-provoking artwork that showcases Mr. Brainwash’s talent for blending different artistic elements to create a truly unique and engaging piece.
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A study of tipping point: much less is known about the most efficient ways to reach such transitions or how self-reinforcing systemic transformations might be instigated through policy. We employ an agent-based model to study the emergence of social tipping points through various feedback loops that have been previously identified to constitute an ecological approach to human behavior. Our model suggests that even a linear introduction of pro-environmental affordances (action opportunities) to a social system can have non-linear positive effects on the emergence of collective pro-environmental behavior patterns.
1. The study of chaos analyzes nonlinear dynamical systems that are highly sensitive to initial conditions. While a universal definition of chaos is still lacking, mathematicians generally agree that chaos involves sensitive dependence on initial conditions, mixing, and dense periodic points.
2. This paper formulates a new approach to studying chaos in discrete dynamical systems based on concepts from inverse problems, set-valued mappings, graphical convergence theory, and topology. The author argues that order, chaos and complexity can be viewed as parts of a unified mathematical structure applying topological convergence theory to increasingly nonlinear mappings.
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This work argues that the emerging understanding of time in quantum information science can be articulated as a philosophical theory of change. Change and time are interrelated, and one can be used to interrogate the other, namely, a theory of change can be derived from a theory of time. What is new in quantum science is time being regarded as just another property to be engineered. At the quantum scale, time is reversible in certain ways, which is quite different from the everyday experience of time whose unidirectional arrow does not allow a dropped egg to reassemble. At the quantum scale of atoms, though, a particle retains the history of its trajectory, which may be retraced before collapsed in measurement.
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This document discusses various models and concepts for analyzing systems, including:
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3. Several mathematical and scientific concepts are proposed as models for linguistic and semantic analysis, such as group theory, topology, Hilbert spaces, and quantum mechanics.
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Mr. Brainwash Beautiful Girl / Mixed Media / signed / Unique
Year: 2023
Format: 96,5 x 127 cm / 37.8 x 50 inch
Material: Fine Art Paper with hand-torn edges.
Method: Mixed Media, Stencil, Spray Paint.
Edition: Unique
Other: handsigned by Mr. Brainwash front and verso.
Beautiful Girl by Mr. Brainwash is a mixed media artwork on paper done in 2023. It is unique and of course signed by Mr. Brainwash. The picture is a tribute to his own most successful work of art, the Balloon Girl. In this new creation, however, the theme of the little girl is slightly modified.
In Mr. Brainwash’s mixed media artwork titled “Beautiful Girl,” we are presented with a captivating depiction of a little girl adorned in a summer dress, with two playful pigtails framing her face. The artwork exudes a sense of innocence and whimsy, as the girl is shown in a dreamy state, lifting one end of her skirt and looking down as if she were about to dance. Through the use of mixed media, Mr. Brainwash skillfully combines different artistic elements to create a visually striking composition. The vibrant colors and bold brushstrokes bring the artwork to life, evoking a sense of joy and happiness. The attention to detail in the girl’s expression and body language adds depth and character to the piece, allowing viewers to connect with the young protagonist on a personal and emotional level. “Beautiful Girl” is a testament to Mr. Brainwash’s unique artistic style, blending elements of street art, pop art, and contemporary art to create a visually captivating and emotionally resonant artwork.
The use of mixed media in “Beautiful Girl” adds an additional layer of complexity to the artwork. By combining different artistic techniques and materials, such as stencils, spray paint, and collage, Mr. Brainwash creates a dynamic and textured composition that grabs the viewer’s attention. The juxtaposition of different textures and patterns adds depth and visual interest to the piece, while also emphasizing the artist’s eclectic and experimental approach to art-making. The inclusion of collage elements, such as newspaper clippings and torn posters, further enhances the artwork’s urban and contemporary feel. Overall, “Beautiful Girl” is a visually captivating and thought-provoking artwork that showcases Mr. Brainwash’s talent for blending different artistic elements to create a truly unique and engaging piece.
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IRIDIA_S12_Morphogenetic_Engineering_slides.pdf
1. VUB AI-Lab / ULB IRIDIA, Winter 2012
“Current Trends in Artificial Intelligence” Course Series
Fall 2011
Complex Systems, Bio-Inspiration
and Morphogenetic Engineering:
New Avenues Toward Self-Organized Architecture
René Doursat
Research Group in Biomimetics, Universidad de Malaga, Spain
Complex Systems Institute Paris, CNRS / CREA, Ecole Polytechnique
4. SWARMORPH: Morphogenesis with Self-Assembling Robots
(M. Dorigo, R. O’Grady et al., IRIDIA, ULB)
SYMBRION: Symbiotic Evolutionary Robot Organisms
(S. Kernbach, T. Schmickl, A. Winfield et al.)
7. Any ideas?
The School of Rock (2003)
Jack Black, Paramount Pictures
1. What are Complex Systems?
8. “simple”
few simple
Emergent
Behavior
Agents /
Parts
Local Rules
Category
A "Complex
System"?
complex
few simple
“simple”
many simple
complex
“complex”
many
many
complicated
simple
deterministic/
centralized
many complicated
A simplified classification of complex systems
1. What are Complex Systems?
9. Few agents, “simple” emergent behavior
Two bodies with similar mass
Wikimedia Commons
Two bodies with different mass
Wikimedia Commons
→ ex: two-body problem
fully solvable and regular trajectories for inverse-square force laws
(e.g., gravitational or electrostatic)
1. What are Complex Systems?
10. Few agents, complex emergent behavior
NetLogo model: /Chemistry & Physics/Mechanics/Unverified Transit orbit of the planar circular restricted problem
Scholarpedia: Three Body Problem & Joachim Köppen Kiel’s applet
→ ex: three-body problem
generally no exact mathematical solution (even in “restricted” case
m1 〈〈 m2 ≈ m3): must be solved numerically → chaotic trajectories
1. What are Complex Systems?
11. Few agents, complex emergent behavior
Logistic map
Baker’s transformation
Craig L. Zirbel, Bowling Green State University, OH
→ ex: more chaos (baker’s/horseshoe maps, logistic map, etc.)
chaos generally means a bounded, deterministic process that is
aperiodic and sensitive on initial conditions → small fluctuations
create large variations (“butterfly effect”)
even one-variable iterative functions: xn+1 = f(xn) can be “complex”
1. What are Complex Systems?
12. Many agents, simple rules, “simple” emergent behavior
Diamond crystal structure
Tonci Balic-Zunic, University of Copenhagen
NetLogo model: /Chemistry & Physics/GasLab Isothermal Piston
→ ex: crystal and gas (covalent bonds or electrostatic forces)
either highly ordered, regular states (crystal)
or disordered, random, statistically homogeneous states (gas):
a few global variables (P, V, T) suffice to describe the system
1. What are Complex Systems?
13. Many agents, simple rules, complex emergent behavior
→ ex: cellular automata, pattern formation, swarm intelligence (insect
colonies, neural networks), complex networks, spatial communities
the “clichés” of complex systems: a major part of this course and
NetLogo models
1. What are Complex Systems?
14. Many agents, complicated rules, complex emergent behavior
→ natural ex: organisms (cells), societies (individuals + techniques)
agent rules become more “complicated”, e.g., heterogeneous
depending on the element’s type and/or position in the system
behavior is also complex but, paradoxically, can become more
controllable, e.g., reproducible and programmable
termite mounds companies techno-networks cities
biological development & evolution
1. What are Complex Systems?
15. Many agents, complicated rules, complex emergent behavior
Swarm chemistry
Hiroki Sayama, Binghamton University SUNY
→ ex: self-organized “artificial life”: swarm chemistry, morphogenesis
in swarm chemistry (Sayama 2007), mixed self-propelled particles with
different flocking parameters create nontrivial formations
in embryomorphic engineering (Doursat 2006), cells contain the same
genetic program, but differentiate and self-assemble into specific shapes
SA
PF
SA4
PF4
SA6
PF6
Embryomorphic engineering
René Doursat, Insitut des Systèmes Complexes, Paris
1. What are Complex Systems?
16. Many agents, complicated rules, “deterministic” behavior
artifacts composed of a
immense number of parts
yet still designed globally
to behave in a limited and
predictable (reliable,
controllable) number of
ways "I don’t want my aircraft to be
creatively emergent in mid-air"
not "complex" systems in
the sense of:
little decentralization
no emergence
no self-organization
Systems engineering
Wikimedia Commons, http://en.wikipedia.org/wiki/Systems_engineering
→ classical engineering: electronics, machinery, aviation, civil construction
1. What are Complex Systems?
17. Many agents, complicated rules, “centralized” behavior
→ spectators, orchestras, military, administrations
people reacting similarly and/or simultaneously to cues/orders
coming from a central cause: event, leader, plan
hardly "complex" systems: little decentralization, little
emergence, little self-organization
1. What are Complex Systems?
18. “simple”
few simple
2-body problem NO
Emergent
Behavior
Agents /
Parts
Local Rules
Category
A "Complex
System"?
complex
few simple
3-body problem,
low-D chaos
NO – too small
“simple”
many simple
crystal, gas NO – few params
suffice to describe it
complex
“complex”
many
many
complicated
simple
structured
morphogenesis
patterns, swarms,
complex networks
YES – reproducible
and heterogeneous
YES – but mostly
random and uniform
deterministic/
centralized
many complicated
machines, crowds
with leaders
COMPLICATED
– not self-organized
Recap: complex systems in this course
1. What are Complex Systems?
19. “simple”
few simple
2-body problem NO
Emergent
Behavior
Agents /
Parts
Local Rules
Category
A "Complex
System"?
complex
few simple
3-body problem,
low-D chaos
NO – too small
“simple”
many simple
crystal, gas NO – few params
suffice to describe it
complex
“complex”
many
many
complicated
simple
structured
morphogenesis
patterns, swarms,
complex networks
YES – reproducible
and heterogeneous
YES – but mostly
random and uniform
deterministic/
centralized
many complicated
machines, crowds
with leaders
COMPLICATED
– not self-organized
Recap: complex systems in this course
1. What are Complex Systems?
20. large number of elementary agents interacting locally
(more or less) simple individual agent behaviors creating
a complex emergent, self-organized behavior
decentralized dynamics: no master blueprint or grand
architect
Complex systems in this course
Internet
& Web
= host/page
insect
colonies
= ant
physical, biological, technical, social systems (natural or artificial)
pattern
formation
= matter
biological
development
= cell
social
networks
= person
the brain
& cognition
= neuron
1. What are Complex Systems?
21. Convection cells in liquid (detail)
(Manuel Velarde, Universidad Complutense, Madrid)
Sand dunes
(Scott Camazine, http://www.scottcamazine.com)
Solar magnetoconvection
(Steven R. Lantz, Cornell Theory Center, NY)
Rayleigh-Bénard convection cells
in liquid heated uniformly from below
(Scott Camazine, http://www.scottcamazine.com)
Physical pattern formation: Convection cells
WHAT?
Hexagonal arrangement of sand dunes
(Solé and Goodwin, “Signs of Life”, Perseus Books)
Schematic convection dynamics
(Arunn Narasimhan, Southern Methodist University, TX)
∆T HOW?
thermal convection, due to temperature gradients, creates stripes and tilings at multiple
scales, from tea cups to geo- and astrophysics
1. What are Complex Systems?
22. Mammal fur, seashells, and insect wings
(Scott Camazine, http://www.scottcamazine.com)
Biological pattern formation: Animal colors
WHAT? ctivator
nhibitor
NetLogo fur coat simulation, after
David Young’s model of fur spots and stripes
(Michael Frame & Benoit Mandelbrot, Yale University)
animal patterns (for warning, mimicry, attraction) can be caused by pigment cells trying to copy
their nearest neighbors but differentiating from farther cells
HOW?
1. What are Complex Systems?
23. Animation of a functional MRI study
(J. Ellermann, J. Strupp, K. Ugurbil, U Minnesota)
WHAT?
the brain constantly
generates patterns of
activity (“the mind”)
they emerge from 100
billion neurons that
exchange electrical signals
via a dense network of
contacts
Spatiotemporal synchronization: Neural networks
Pyramidal neurons & interneurons
(Ramón y Cajal 1900)
Cortical layers
HOW?
Schematic neural network
1. What are Complex Systems?
24. Swarm intelligence: Insect colonies (ant trails, termite mounds)
Termite mound
(J. McLaughlin, Penn State University)
http://cas.bellarmine.edu/tietjen/
TermiteMound%20CS.gif
Termite stigmergy
(after Paul Grassé; from Solé and Goodwin,
“Signs of Life”, Perseus Books)
termite colonies
build complex
mounds by
“stigmergy”
Harvester ant
(Deborah Gordon, Stanford University)
http://taos-telecommunity.org/epow/epow-archive/
archive_2003/EPOW-030811_files/matabele_ants.jpg
http://picasaweb.google.com/
tridentoriginal/Ghana
HOW?
WHAT?
ants form trails by
following and
reinforcing each
other’s pheromone
path
1. What are Complex Systems?
25. Bison herd
(Center for Bison Studies, Montana State University, Bozeman)
Fish school
(Eric T. Schultz, University of Connecticut)
Collective motion: flocking, schooling, herding
WHAT?
Separation, alignment and cohesion
(“Boids” model, Craig Reynolds, http://www.red3d.com/cwr/boids)
S A C
each individual adjusts its
position, orientation and
speed according to its
nearest neighbors
HOW?
coordinated collective
movement of dozens or
1000s of individuals
(confuse predators, close in
on prey, improve motion
efficiency, etc.)
1. What are Complex Systems?
26. Complex networks and morphodynamics: human organizations
SimCity (http://simcitysocieties.ea.com)
organizations urban dynamics
(Thomas Thü Hürlimann, http://ecliptic.ch)
NSFNet Internet (w2.eff.org)
techno-social
networks
global connectivity
WHAT?
NetLogo urban sprawl simulation
NetLogo preferential attachment simulation
cellular automata model
“scale-free” network model
HOW?
1. What are Complex Systems?
27. 1. What Are Complex Systems?
the brain
organisms
ant trails
termite
mounds
animal
flocks
cities,
populations
social networks
markets,
economy
Internet,
Web
physical
patterns
living cell
biological
patterns
animals
humans
& tech
molecules
cells
All agent types: molecules, cells, animals, humans & tech
28. Categories of complex systems by range of interactions
the brain
organisms
ant trails
termite
mounds
animal
flocks
physical
patterns
living cell
biological
patterns
2D, 3D spatial
range
non-spatial,
hybrid range cities,
populations
social networks
markets,
economy
Internet,
Web
1. What Are Complex Systems?
29. the brain
organisms
ant trails
termite
mounds
animal
flocks
physical
patterns
living cell
biological
patterns
cities,
populations
social networks
markets,
economy
Internet,
Web
Natural and human-caused categories of complex systems
... yet, even human-caused
systems are “natural” in the
sense of their unplanned,
spontaneous emergence
1. What Are Complex Systems?
30. Emergence on multiple levels of self-organization
1. What Are Complex Systems?
complex systems:
a) a large number of elementary agents
interacting locally
b) simple individual behaviors creating a
complex emergent collective behavior
c) decentralized dynamics: no master
blueprint or grand architect
31. From genotype to phenotype, via development
1. What Are Complex Systems?
× ×
→ →
32. 1. What Are Complex Systems?
.
.
.
.
.
.
Ramón y
Cajal 1900
From neurons to brain, via neural development (anatomy)
33. 1. What Are Complex Systems?
ctivator
nhibitor
From pigment cells to coat patterns, via reaction-diffusion
34. From social insects to swarm intelligence, via stigmergy
1. What Are Complex Systems?
35. From birds to flocks, via flocking
1. What Are Complex Systems?
separation alignment cohesion
36. there are a lot of theories and results in related disciplines (“systems
theory”, “computational complexity”, etc.), yet
such generic names often come from one researcher with one particular view
there is no unified viewpoint on complex systems, especially autonomous
in fact, there is not even any agreement on their definition
So, there is no general “complex systems science” or
“complexity theory”...
we are currently dealing with an intuitive set of criteria, more or less
shared by researchers, but still hard to formalize and quantify:
complexity
emergence
self-organization
multitude / decentralization
adaptation, etc.
1. What Are Complex Systems?
40. 1. What are Complex Systems?
dynamics: behavior and activity of a
system over time multitude, statistics: large-scale
properties of systems
adaptation: change in typical
functional regime of a system
systems sciences: holistic (non-
reductionist) view on interacting parts
complexity: measuring the length to describe,
time to build, or resources to run, a system
A vast archipelago of precursor and neighboring disciplines
different families of disciplines focus on different aspects
(naturally, they intersect a lot: don’t take this taxonomy too seriously)
41. dynamics: behavior and activity of a
system over time multitude, statistics: large-scale
properties of systems
adaptation: change in typical
functional regime of a system
complexity: measuring the length to describe,
time to build, or resources to run, a system
dynamics: behavior and activity of a
system over time
nonlinear dynamics & chaos
stochastic processes
systems dynamics (macro variables)
adaptation: change in typical
functional regime of a system
evolutionary methods
genetic algorithms
machine learning
complexity: measuring the length to describe,
time to build, or resources to run, a system
information theory (Shannon; entropy)
computational complexity (P, NP)
Turing machines & cellular automata
systems sciences: holistic (non-
reductionist) view on interacting parts
systems sciences: holistic (non-
reductionist) view on interacting parts
systems theory (von Bertalanffy)
systems engineering(design)
cybernetics (Wiener; goals & feedback)
control theory (negative feedback)
→ Toward a unified “complex
systems” science and
engineering?
multitude, statistics: large-scale
properties of systems
graph theory & networks
statistical physics
agent-based modeling
distributed AI systems
1. What are Complex Systems?
A vast archipelago of precursor and neighboring disciplines
42. 1. What are Complex Systems?
• Decentralization
• Emergence
• Self-organization
5. Bio-Inspiration and
Artificial Evo-Devo
Or how to control
spontaneity
ARCHITECTURE & SELF-ORGANIZATION
43. Between natural and engineered emergence
CS engineering: creating and programming
a new "artificial" emergence
→ Multi-Agent Systems (MAS)
CS science: observing and understanding "natural",
spontaneous emergence (including human-caused)
→ Agent-Based Modeling (ABM)
CS computation: fostering and guiding
complex systems at the level of their elements
5. Bio-Inspiration and Artificial Evo-Devo
But CS computation is
not without paradoxes:
• Can we plan
autonomy?
• Can we control
decentralization?
• Can we program
adaptation?
44. ex: genes & evolution
laws of genetics
genetic program,
binary code, mutation
genetic algorithms (GAs),
evolutionary computation
for search & optimization
ex: neurons & brain
biological neural models
binary neuron,
linear synapse
artificial neural networks
(ANNs) applied to machine
learning & classification
ex: ant colonies
trail formation, swarming
agents that move, deposit
& follow “pheromone”
ant colony optimization (ACO)
applied to graph theoretic
& networking problems
Exporting models of natural complex systems to ICT
already a tradition, but mostly in offline search and optimization
TODAY: simulated in a Turing machine / von Neumann architecture
ABM
MAS
5. Bio-Inspiration and Artificial Evo-Devo
45. Exporting natural complex systems to ICT
... looping back onto unconventional physical implementation
genetic algorithms (GAs),
evolutionary computation
for search & optimization
artificial neural networks
(ANNs) applied to machine
learning & classification
ant colony optimization (ACO)
applied to graph theoretic
& networking problems
DNA computing
synthetic biology
chemical, wave-based
computing
TOMORROW: implemented in bioware, nanoware, etc.
5. Bio-Inspiration and Artificial Evo-Devo
46. ... or bioware, nanoware, etc.
whether Turing machine...
genetics evolution
Nadine
Peyriéras,
Paul
Bourgine
et
al.
(Embryomics
&
BioEmergences)
evolution
Ulieru
&
Doursat
(2010)
ACM
TAAS
simulation
by
Adam
MacDonald,
UNB
Doursat (2008)
ALIFE XI, WInchester
A new line of bio-inspiration: biological morphogenesis
designing multi-agent models for decentralized systems engineering
Morphogenetic
Engineering
5. Bio-Inspiration and Artificial Evo-Devo
development
47. 1. What are Complex Systems?
• Decentralization
• Emergence
• Self-organization
2. Architects Overtaken
by their Architecture
Designed systems that
became suddenly complex
Complex systems seem so different from architected systems, and yet...
5. Bio-Inspiration and
Artificial Evo-Devo
Or how to control
spontaneity
3. Architecture Without
Architects
Self-organized systems that
look like they were designed
ARCHITECTURE & SELF-ORGANIZATION
48. cities,
populations
Internet,
Web social networks
markets,
economy
companies,
institutions
address
books
houses,
buildings
computers,
routers
2. Architects Overtaken by their Architecture
At large scales, human superstructures are "natural" CS
... arising from a multitude of
traditionally designed artifacts
houses, buildings
address books
companies, institutions
computers, routers
large-scale
emergence
small to mid-
scale artifacts
by their unplanned, spontaneous
emergence and adaptivity...
geography: cities, populations
people: social networks
wealth: markets, economy
technology: Internet, Web
49. 2. Architects Overtaken by their Architecture
At mid-scales, human artifacts are classically architected
a goal-oriented, top-down process toward
one solution behaving in a limited # of ways
specification & design: hierarchical view of
the entire system, exact placement of elts
testing & validation: controllability, reliability,
predictability, optimality
ArchiMate
EA
example
the (very) "complicated" systems of classical
engineering and social centralization
electronics, machinery, aviation, civil
construction, etc.
spectators, orchestras, administrations,
military (reacting to external cues/leader/plan)
not "complex" systems:
little/no decentralization, little/no emergence,
little/no self-organization
New inflation: artifacts/orgs made of a huge number of parts
Systems
engineering
Wikimedia
Commons
50. ... and enterprise architecture?
number of transistors/year
in hardware, software,
agents, objects, services
number of O/S lines of code/year
networks...
number of network hosts/year
Burst to large scale: de facto complexification of ICT systems
ineluctable breakup into, and proliferation of, modules/components
2. Architects Overtaken by their Architecture
→ trying to keep the lid on complexity won’t work in these systems:
cannot place every part anymore
cannot foresee every event anymore
cannot control every process anymore ... but do we still want to?
51. Large-scale: de facto complexification of organizations, via
techno-social networks
ubiquitous ICT capabilities connect people and infrastructure in
unprecedented ways
giving rise to complex techno-social "ecosystems" composed of a
multitude of human users and computing devices
2. Architects Overtaken by their Architecture
→ in this context, impossible to assign every single participant a predetermined role
healthcare energy & environment
education defense & security
business finance
from a centralized oligarchy of providers of
data, knowledge, management, information, energy
to a dense heterarchy of proactive participants:
patients, students, employees, users, consumers, etc.
explosion in size and complexity in all domains of society:
52. 3. Architecture Without
Architects
Self-organized systems that
look like they were designed
1. What are Complex Systems?
• Decentralization
• Emergence
• Self-organization
2. Architects Overtaken
by their Architecture
Designed systems that
became suddenly complex
but were not
Complex systems seem so different from architected systems, and yet...
5. Bio-Inspiration and
Artificial Evo-Devo
Or how to control
spontaneity
ARCHITECTURE & SELF-ORGANIZATION
53. ... yet, even human-caused
systems are "natural" in the
sense of their unplanned,
spontaneous emergence
the brain organisms ant trails
termite
mounds
animal
flocks
physical
patterns
living cell
biological
patterns
biology strikingly demonstrates
the possibility of combining
pure self-organization and
elaborate architecture, i.e.:
a non-trivial, sophisticated morphology
hierarchical (multi-scale): regions, parts, details
modular: reuse of parts, quasi-repetition
heterogeneous: differentiation, division of labor
random at agent level, reproducible at system level
3. Architecture Without Architects
"Simple"/random vs. architectured complex systems
54. Ex: Morphogenesis – Biological development
cells build
sophisticated
organisms by
division, genetic
differentiation and
biomechanical self-
assembly
Nadine Peyriéras, Paul Bourgine et al.
(Embryomics & BioEmergences)
www.infovisual.info
architecture
Termite mound
(J. McLaughlin, Penn State University)
http://cas.bellarmine.edu/tietjen/
TermiteMound%20CS.gif
Termite stigmergy
(after Paul Grassé; from Solé and Goodwin,
"Signs of Life", Perseus Books)
termite colonies
build sophisticated
mounds by
"stigmergy" = loop
between modifying
the environment
and reacting
differently to these
modifications
Ex: Swarm intelligence – Termite mounds
architecture
3. Architecture Without Architects
55. Complex systems can possess a strong architecture, too
"complex" doesn’t imply "flat"...
→ modular, hierarchical, detailed architecture
"complex" doesn’t imply "random"...
→ reproducible patterns relying on programmable agents
"complex" doesn’t imply "homogeneous"...
→ heterogeneous agents and diverse patterns, via positions
→ cells and social insects have successfully "aligned business and
infrastructure" for millions of years without any architect telling them how to
transport
royal
chamber
fungus
gardens
reproduce
defend
queen
ventilation shaft
worker
soldier
nursery galleries
build
(mockup) EA-style diagram of a termite mound
architecture
but then what does it
mean for a module to
be an "emergence" of
many fine-grain agents?
3. Architecture Without Architects
56. Pattern Formation → Morphogenesis
“I have the stripes, but where is the zebra?” OR
“The stripes are easy, it’s the horse part that troubles me”
—attributed to A. Turing, after his 1952 paper on morphogenesis
3. Architecture Without Architects
57. more
self-organization
more
architecture
gap to fill
Many self-organized systems exhibit random patterns...
... while "complicated" architecture is designed by humans
(a) "simple"/random self-organization
(d) direct
design
(top-down)
NetLogo simulations: Fur, Slime, BZ Reaction, Flocking, Termite, Preferential Attachment
3. Architecture Without Architects
58. artificial
natural
(b) natural
self-organized
architecture
(c) engineered
self-organization
(bottom-up)
. . . .
. . . .
more
self-organization
more
architecture
Many self-organized systems exhibit random patterns...
Can we transfer some of their principles to human-made
systems and organizations?
The only natural emergent and structured CS are biological
self-forming robot swarm
self-programming software
self-connecting micro-components
self-reconfiguring manufacturing plant
self-stabilizing energy grid
self-deploying emergency taskforce
self-architecting enterprise
SYMBRION
Project
3. Architecture Without Architects
59. 3. Architecture Without
Architects
Self-organized systems that
look like they were designed
1. What are Complex Systems?
• Decentralization
• Emergence
• Self-organization
2. Architects Overtaken
by their Architecture
Designed systems that
became suddenly complex
4. Morphogenetic
Engineering
From cells and insects to
robots and networks
but were not
5. Bio-Inspiration and
Artificial Evo-Devo
Or how to control
spontaneity
ARCHITECTURE & SELF-ORGANIZATION
60. Sculpture → forms
Painting → colors
the forms are
“sculpted” by the self-
assembly of the
elements, whose
behavior is triggered
by the colors
new color regions
appear (domains of
genetic expression)
triggered by
deformations
“patterns from shaping”
“shape from patterning”
Ádám
Szabó,
The
chicken
or
the
egg
(2005)
http://www.szaboadam.hu
A closer look at morphogenesis: it couples assembly and patterning
4. Morphogenetic Engineering
61. Genetic regulation
PROT A PROT B
GENE I
PROT C
"key"
"lock"
schema after Carroll, S. B. (2005)
“Endless Forms Most Beautiful”, p117
GENE A
GENE B
GENE C
A
B
X
Y
I
Tensional
integrity
Donald
Ingber,
Harvard
Cellular
Potts
model
Graner,
Glazier,
Hogeweg
http://www.compucell3d.org
GENE I
Drosophila
embryo
GENE C
GENE A
GENE B
Deformable
volume
Doursat,
simul.
by
Delile
A closer look at morphogenesis: ⇔ it couples mechanics and genetics
Spring-mass
model
Doursat
(2009)
ALIFE
XI
Cellular mechanics
adhesion
deformation / reformation
migration (motility)
division / death
4. Morphogenetic Engineering
62. grad1
div1
patt1
div2
grad2
patt2
div3
grad3
patt3
...
Alternation of self-
positioning (div)
and self-
identifying
(grad/patt)
genotype
Capturing the essence of morphogenesis in an Artificial Life agent model
each agent
follows the same set
of self-architecting rules (the "genotype")
but reacts differently depending on its neighbors
4. Morphogenetic Engineering
Doursat (2009)
18th GECCO
67. I9
I1
(a) (b)
(c)
. . . . . .
WE = X NS = Y
B1 B2 B3 B4
I3 I4 I5
X Y
. . . I3 I4 I5 . . .
B1 B2 B4
B3
wiX,Y
GPF
wki
Programmed patterning (patt): the hidden embryo atlas
a) same swarm in different colormaps to visualize the agents’ internal
patterning variables X, Y, Bi and Ik (virtual in situ hybridization)
b) consolidated view of all identity regions Ik for k = 1...9
c) gene regulatory network used by each agent to calculate its expression
levels, here: B1 = σ(1/3 − X), B3 = σ(2/3 − Y), I4 = B1B3(1 − B4), etc.
4. Morphogenetic Engineering
68. p
A
B
V
r
r0
re
rc
div
GSA : rc < re = 1 << r0
p = 0.05
I4 I6
B4
B3
grad patt
E
W
S
N
E
W
WE WE
NS
X Y
. . . I3 I4 I5 . . .
B1 B2 B4
B3
wix,iy
GPF : {w }
wki
WE NS
Doursat (2008)
ALIFE XI
GSA ∪ GPF
69. details are not created in one shot, but gradually added. . .
. . . while, at the same time, the canvas grows
from Coen, E. (2000)
The Art of Genes, pp131-135
4. Morphogenetic Engineering
Morphological refinement by iterative growth
70. I4 I6
E(4)
W(6)
I5
I4
I1
N(4)
S(4)
W(4) E(4)
rc = .8, re = 1, r0 = ∞
r'e= r'0=1, p =.01
GSA
SA
PF
SA4
PF4
SA6
PF6
4. Morphogenetic Engineering: Devo
SA
PF
SA4
PF4
SA6
PF6
all cells have same GRN, but execute different
expression paths → determination / differentiation
microscopic (cell) randomness, but
mesoscopic (region) predictability
Doursat (2008)
ALIFE XI, WInchester
72. Quantitative mutations: limb thickness
GPF
GSA
3×3
1, 1
p = .05
g = 15
4 6
disc
GPF
GSA
1×1
tip p’= .05
g’= 15
GPF
GSA
1×1
tip p’= .05
g’= 15
GPF
GSA
3×3
2, 1 4 6
disc
p = .05
g = 15
GPF
GSA
1×1
tip p’= .05
g’= 15
GPF
GSA
3×3
0.5, 1 4 6
disc
p = .05
g = 15
(a) (b) (c)
wild type thin-limb thick-limb
body plan
module
limb
module
4 6
4. Morphogenetic Engineering: Devo-Evo
73. (a) (b) (c)
antennapedia duplication
(three-limb)
divergence
(short & long-limb)
PF
SA
1×1
tip p’= .05
GPF
GSA
3×3
p = .05
4 2
disc
6
PF
SA
1×1
tip p’= .1
PF
SA
1×1
tip p’= .03
GPF
GSA
3×3
p = .05
4 2
disc
6
GPF
GSA
1×1
p’= .05
tip
GPF
GSA
3×3
p = .05
4 2
disc
GPF
GSA
1×1
p’= .05
tip
4
2
6
Qualitative mutations: limb position and differentiation
antennapedia homology by duplication divergence of the homology
4. Morphogenetic Engineering: Devo-Evo
74. production
of structural
innovation
Changing the agents’ self-architecting rules through evolution
by tinkering with the genotype, new architectures (phenotypes) can be obtained
Doursat (2009)
18th GECCO, Montreal
4. Morphogenetic Engineering: Devo-Evo
75. 75
(Delile,
Doursat,
Peyrieras)
More accurate mechanics
3-D
individual cell shapes
collective motion, migration
adhesion
Better gene regulation
recurrent links
gene reuse
kinetic reaction ODEs
attractor dynamics
switch
combo 1
switch
combo 2
after
David
Kingsley,
in
Carroll,
S.
B.
(2005)
Endless
Forms
Most
Beautiful,
p125
4. Morphogenetic Engineering: Devo-MecaGen
77. 4. Morphogenetic Engineering: Devo-Bots
Morphogenetic swarm robotics: toward structured robot
flocking
using “e-pucks”
Current collaboration with
• Alan Winfield, Bristol Robotics Lab, UWE
• Wenguo Liu, Bristol Robotics Lab, UWE
78. La prise en compte du spatial
[Même] si pour l'instant la biologie synthétique se focalise sur la «
programmation d'une seule bactérie », le développement de biosystèmes un
tant soit peu complexe reposera sur le fonctionnement intégré de colonies
bactériennes et donc sur la prise en compte d'interactions spatiales au sein
d'une population de cellules différenciées. [...]
La maîtrise des interactions spatiales ouvre la voie à une ingénierie du
développement [biologique], ce qui permet de rêver à des applications qui
vont bien au-delà de la conception de la cellule comme « usine chimique ».
Projet SynBioTIC, 2010
ANR Project with (among others)
• Jean-Louis Giavitto, ex-IBISC, Evry
• Oliver Michel, A. Spicher, LACL, Creteil
• Franck Delaplace, Evry ... et al.
Synthetic Biological SysTems: from DesIgn to Compilation
PROTO
ex: spatial computing languages: PROTO (Beal) and MGS (Giavitto)
4. Morphogenetic Engineering: Devo-SynBioTIC
81. freely growing
structure
Evolution: inventing new architectures
"wildtype"
ruleset A
ruleset A
(b) (b)
ruleset A’
ruleset A"
Polymorphism: reacting and adapting to the environment
Development: growing an intrinsic architecture
Ulieru
&
Doursat
(2010)
ACM
TAAS
simulation
by
Adam
MacDonald,
UNB
82. Afer 20 mutations :
New set of rules :
open left port
if a rock is on the left then open top port
if a rock is on the right then close top port
Starting rules :
open left port, open right port
Mutations of the rules
Creation
Deletion
Modification
Order change
Constants’ change
4. Morphogenetic Engineering: ProgNet
with David Fourquet
83. N=0
N = 100 mutations
N = 300 mutations
if top gradient = 0 mod (width) open right port
if right gradient = 0 modulo (periodicity) open top port
if rock on the left open top port
if rock on the right open left port
width = 8, periodicity = 5
if top gradient = 0 mod (width) open right port
if right gradient = 0 modulo (periodicity) open top port
if top gradient = width close top port
if right gradient = length close right port
if rock on top open left port
if bottom gradient > new_cst and rock at right open left port
length = 20 , width = 8, periodicity = 5, new_cst = 9
if left gradient = 0 open right port
if right gradient = 0 modulo (periodicity) open top port
if top gradient > 0 close right port
if top gradient = width open right port
if right gradient >= length close right port
if top gradient >= width close top port
length = 20 , width = 10, periodicity = 5
84. Engineering Complex Socio-
Technical Adaptive SYstems
Submitted FET-ICT Open Project with
• Jeremy Pitt, Imperial College, London
• Andrzej Nowak, U Warsaw
• Mihaela Ulieru, Canada Research Chair
The ECSTASY project is about the science of socio-technical combinatorics
underpinning the ICT for engineering such scenarios.
We define socio-technical combinatorics as the study of the potentially infinite
number of discrete and reconfigurable physical, behavioural and
organisational structures which characterise socio-technical systems
comprising humans, sensors, and agents.
It is also the study of how these structures interact with each other and their
environment – how they assemble, evolve, dis-assemble, and re-assemble,
and how they can be engineered.
Projet ECSTASY, 2011
4. Morphogenetic Engineering: ECSTASY
85. 4. Morphogenetic Engineering (ME)
a) Giving agents self-identifying and self-positioning abilities
agents possess the same set of rules but execute different subsets
depending on their position = "differentiation" in cells, "stigmergy" in insects
b) ME brings a new focus on "complex systems engineering"
exploring the artificial design and implementation of autonomous systems
capable of developing sophisticated, heterogeneous morphologies or
architectures without central planning or external lead
Summary: ME is about programming the agents of emergence
swarm robotics,
modular/reconfigurable robotics
mobile ad hoc networks,
sensor-actuator networks
synthetic biology, etc.
c) Related emerging ICT disciplines and application domains
amorphous/spatial computing (MIT)
organic computing (DFG, Germany)
pervasive adaptation (FET, EU)
ubiquitous computing (PARC)
programmable matter (CMU)
86. http://iscpif.fr/MEW2009
1st “Morphogenetic Engineering” Workshop, ISC,Paris 2009
http://iridia.ulb.ac.be/ants2010
2nd “Morphogenetic Engineering” Session, ANTS 2010, Brussels
“Morphogenetic Engineering” Book, 2012, Springer
R. Doursat, H. Sayama & O. Michel, eds.
http://ecal11.org/workshops#mew
3rd “Morphogenetic Engineering” Workshop, ECAL 2011, Paris
4. Morphogenetic Engineering (ME)
87. 4. Morphogenetic
Engineering
From cells and insects to
robots and networks
5. Bio-Inspiration and
Artificial Evo-Devo
Or how to control
spontaneity
3. Architecture Without
Architects
Self-organized systems that
look like they were designed
1. What are Complex Systems?
• Decentralization
• Emergence
• Self-organization
2. Architects Overtaken
by their Architecture
Designed systems that
became suddenly complex
but were not
ARCHITECTURE & SELF-ORGANIZATION