AADRL PHASE 1 PROTOCELL by Oğulcan Suluçay

AA School Design Research Laboratory

protocell.

Architectural Association School of Architecture

VOLUME II

PROTOCELL. STUDIO SPYROPOULOS

Tutors Theodore Spyropoulos, Course Tutor / Director AADRL, Mostafa El-Sayed, Technical Tutor Apostolos Despotidis, Technical Tutor

ARCHITECTURAL ASSOCIATION

Team Ece Bahar Elmaci Badanoz (Turkey) Lara Niovi Vartziotis (Greece) Ogulcan Sulucay (Turkey) Qiquan Lu (China)

STUDIO BRIEF

Sypropoulos Studio within the AA Design Research Laboratory questions what culture is and how architecture will take a stand on the context of culture. In the process of our studio research, machine learning is determined as a key component to investigate aspects of culture, intelligence and in which ways it will redefine architecture in the contemporary context. For each team, it is crucial to understand significance of culture and what it represents. It is misleading to situate culture as a mere production of artifacts or just a set of similarities among regions in terms of learned behaviors. Culture represents an evolutionary process of humanity. In other words, it is a way of communication, a set of rules that evolves and aggregates through time. As a team it is critical for us to see our project as an evolutionary system which aggregates with multiple experiments and evolves step by step. Every step is aiming to question how agents and environment can be set in virtual environment through the steps of optimization, systematization, simulation and eventually machine learning, to create an aspect of culture through interactions and communication among agents throughout the research.

INDEX

Introduction

Thesis Statement

Mobility Urban Mobility and Infrastructure The Map

18 20

Communication

Learning Self-Organization

Behaviour Connectivity Recognition Machine Learning Types of Machine Learning Approach Experiments Materiality Pneumatic models Origami folding Surface geometry Folding

42 54 60 66 68

78 82 88 90

Self-Organization Packing Local Packing Organizational Packing Reaction-diffusion Systems Reaction-diffusion Models Ferro-fluid Experiments

92 98 100 106 115 116

Architectural Proposal Density Map City Scale Organization Aerial Perspective

122 124 128

Bibliography

131

Technology is Culture “ We have a habit - long cultivated - of imagining them as separate, the two great tributaries rolling steadily to the sea of modernity, and dividing everyone in their path into two camps: those that dwell on the shores of technology and those that dwell on the culture. ... But it is as false as the genetic separation between human and ape. As was true for the triumph of Darwinism in this century, it has taken a combined effort of art and science to make this error visible. “

In most generic explanation culture is the way of life. In a higher perspective it can be evaluated as a collection of routines, information and senses from the moment humans started to reflect their experiences. Which throughout the history articulated in many different forms, as cave paintings [Fig.2], books and now through media. These can be understood as technological developments. However, if technology can change how people express themselves or the way they live how it can be so apart from the culture? Literature, artworks, design all these can be easily identified as an cultural aspect. Than how come technology and culture is being seen as two complete opposites? As it is stated before there is no such difference. Technology is culture. Every innovative idea, analytical approach to environment results with a development. In that case how a ground breaking artwork is different than responding a lacking feature of the way we do things and change the functionality of the world. Apart from technology and culture is in a cycle by constantly feeding and improving each other being innovative and able to change the perspective makes technology a culture. “At no period in human culture have men understood the phychic mechanisms involved in invention and technology. Today it is the instant speed of electric information that, for the first time, permits easy recognition of the patterns and the formal contours of change and development. The entire world, past and present, now reveals itself to us like a growing plant in an enormously accelerated movie. Electric speed is synonymous with light and with the understanding of causes.”

Speed is highly related with innovation and being responsive. Technology the way its related with culture has to be innovative and responsive in the way it interacts with the environment. Which affects the way we live, experience and express ourselves. Changes the culture, defines the way we live.

INTRODUCTION

Alphaville (1965) - Jean-Luc Godard Alphaville [Fig.1] is a movie by Jean-Luc Godard which he intended the name “IBM versus Tarzan” in the first place. In general it is about a city and people who are controlled by a computer and “sterilized from their emotions“ by it. While the movie has a paranoid approach to technology as a culture, it is important in a way that there is no separation. While this movie inspired lots of science fiction movies like “Blade Runner” and “Dark City“. The dystopian environments created in those movies are similar to the one in Alphaville, technology driven culture however in a paranoid, something so disconnected from the way how the technology and culture is overlapping. Fig.1

“ Technology used to advance in slower, more differentiated stages. The book reigned as the mass medium of choice for several centuries; newspapers had a couple of hundred years to innovate; eve film ruled the roost for thirty years before the rapid-fire succession of radio, then television, then the personal computer. With each innovation, the gap that kept the past at bay grew shorter, more attenuated.“

Technology allows people to recognize the changes, compare and challenge everything around our environment. By challenging tangible and abstract every aspect in the envirwonment it allows society to create, enhance and articulate its culture. Faster the process more understandable the environment becomes therefore, the gap between before and after becomes less perceivable. It is easier to relate technology with culture today, as technology is faster than ever, we live in constant change.

Fig.2

Fig.3

Interface as a Medium “Information needs physical medium in order to be articulated, whether this is the chemistry of an organism, a physical surface to inscribe marks on with a physical instrument, or the liquid crystals to depict pixels on a screen. Thus, the ways in which the city is shaped by the creation and distribution of data is closely linked to the question of how the infrastructure that mediates information and the city is formatted and consequently experienced by its users.“

To be able to represent or reach over to a certain information is important to evaluate or interpret more out of it. By all means interfaces create a medium to discuss and articulate data. Through that interaction opportunities are created to be innovative and responsive about the environment. It is as important as understanding the relation between technology and culture. Because when interface understood as a tool to control technology it overrides all the possibilities to be innovative and creative. Opposing to that perspective, it should be considered as a medium to interact, discuss and share information in many forms.

“For the first twenty years of interface design, the dominant model was architectural: interfaces imagined binary code as a space, something to be explored. The new interface paradigm brings us closer to Olimpia’s (a character - life like doll - from Hoffman’s Sandman) glassy stare instead of space, those zeros and ones are organized into something closer to an individual, with a temperament,a physical appareance, an aptitude for learning - the computer as personality, nor space. ”

Even though the design process of interface indicates something more architectural or complex it should be understood as a medium to interact, that creates possibilities to be involved and represent. Like technology is culture, interface is the medium to be present and creative over that culture. Through the interaction with the interfaces every information can be made visible, or even be transformed something more than it indicates by itself.

INTRODUCTION

Fig.4

TUNEABLE TOUCH Tuneable Touch [Fig.4] provides an alternative configuration to what the majority of us experience as the material world. To that end, it employs the sense of touch, to radio waves which it is impossible to sense without this interface. KAZAMIDORI Kazamidori [Fig.5] was a device to indicate the social wind of interests on the Internet. When somebody visited the Ars Electronica website, “Kazamidori“ turned to point in the direction of the visitor. For example, if somebody visited from Tokyo, “Kazamidori“ would point that direction.

Fig.5

Architecture as Infrastructure

“Information that shapes the city: its sensory experience, its infrastructure and its places. We are interested in the ways different groups use urban information to make sense of public spaces and change them. Therefore two processes are of special interest: 1. Sensing Place: The role of urban data as a public good in a context where cities are increasingly instrumented with real-time sensor networks 2. Placing Sense: Practices aimed at changing the urban environment by re-inscribing public spaces through location-based media.”

Today, we don’t place sense into city through our experiences in the first place. To understand or set a pin point to a place we use multiple layers of data which can be either geo-location, raw data or shared experience of someone else. After these processes we enhance the received information with our experiences. Which is a unique and valuable process because it can be shared and change the perception of that place for someone else. Moreover it completely changes how society experience places and the dynamics of the city.

“The mental image of the city has become more complex - multiple layers of data seem to float above the physical environment, and ubiquitous technologies are changing our experience of the city by allowing us to access to those informational infrastructures. As Bill Mitchell argues, we have entered an era of electronically extended bodies which need to navigate both the electronically mediated environments and the tangible world in parallel.(Mitchell, 1995, p.167) ”

Through our data infrastructure, whole image and pattern of the city changes. Time, population, trends or physical infrastructure of the city all effects the active pattern of the city. More complex that it gets infrastructures of the city are getting more blended into each other. As a whole it creates a system beyond perception and creates a new solution to respond each demand. In this system, architecture should take place beyond than just a node in the network. It should perform as an infrastructure to respond the different demands from each system, which creates a synthesis with each infrastructure by cooperating and blending in to our routines.

INTRODUCTION

Fig.6

Fig.7

SENSE OF PATTERNS Sense of Patterns [Fig.6-Fig7] is a project by Mahir M. Yavuz, a series of printed data visualizations that marks the the behaviors of masses in selected public spaces in Vienna. The observations resolves patterns of moving entities in public such as trains, personal cars, busses and taxis as well as the interactions between these transportation systems. It also analyzes the quality of existing infrastructures of the city. The project managed to create very strong and informative visuals abaout urban infrastructure and mobility patterns. LOS OJOS DEL MUNDO This projects [Fig.8] intends to unfold tourist trends and interests by data mining publicly shared photos of people who has visited Spain and mapping the locations. Through the collection of data and variety of visualization techniques, it uncovers the evolutions of the presence and flows of tourists.

Fig.8 los ojos del mundo - MIT Sensable City Lab

THESIS STATEMENT

Within the modern cities public spaces is a vital component of its dynamics and functionality such as roads, rails and public transformation infrastructures. Not only they occupy significantly large space in the city, also creates an interface for urban culture to constantly change and evolve. Mobility as a function of our lives has a huge impact in lives of people and routines. Nowadays being mobile is in the core of urban culture. While urban mobility highly dependent of its designated infrastructure, all the components of that infrastructure such as streets, pavements, rails acts as an interface that regulates urban life and creates a medium for interaction. In this manner approaching this vital interface for urban culture as a technical issue to solve and trying to make it as invisible as possible is not overlapping with the contemporary dynamics of the city. Interface / infrastructure for mobility is not just a congestion problem that needs to be solved it is a medium to be designed, which needs to be adapt and evolve with the urban culture as much as it creates an interface for it. Protocell is a system that operates on the existing infrastructure for urban mobility treats it as a public space by enhancing and augmenting it according to the user demands and daily routines. It is a curator for urban culture, creating its own infrastructure and enhance the existing qualities of the city. For such capacity to receive data from the city as much as its users, the system needs to be able to recognize its environment and learn through it. Therefore the system starts to evolve and perform according to the urban culture and generate adaptive behaviours. Through learning and communicating with its user Protocells curate the city and redefines architecture as an infrastructure as well. By allowing urban mobility to operate not accordingly to its designated infrastructure but self-organizing it, as much as it creates a reflection of culture over the urban pattern, Protocell becomes an interface for the formation, organization and construction of the city.

From the industrial era, mobility has been a huge part of urban life. People are spending substantial amount of their times in streets, walking, cycling using their personal vehicles or interacting with city through public transportation. One of the crucial thing that needs to be mentioned though, especially urban mobility is highly dependent on its infrastructure. It is almost settling the way people perform their routines, according to congestion or time that selected transportation system requires. Therefore the way people become mobile is accordingly to the designated infrastructure. The only way people can become mobile independent to an infrastructure is through their body, by walking.

The history of mobility, thus the history of the use of cast iron and steel in architecture, the introduction of the elevator, the electrification of the railway system, the introduction of the automobile as a private means of transportation, the spread of air traffic, the invention of the refrigerated shipping containers, and, finally, of the so-called revolution of informatics and telematics.

If we consider transportation infrastructure as a network we can say the linear tracks that intersect, creates an opportunity to expand. Which reflects to city as a station, junction or a stop. In that sense those intersection or infrastructure as a whole can be understood as an interface which creates a medium to interact and influence other systems. For example transportation infrastructure systems have great influence on architecture, it almost settles the value, accessibility, size and functionality of it in many ways. Before elevators or any form of vertical mobility infrastructure, there was no concept of high rise cities. In history people always build bigger in size than before to infuse power, but being vertically mobile change the way we design. However, in contemporary world city is defined by users over shared information and experience in each day. The way we understand and design our built environment is changing as fast as the information infrastructure allows.

Fig.9

Fig.10

MOBILITY URBAN MOBILITY & INFRASTRUCTURE A node is an intersection of two modes or two modal lines and is, thus, an potential interchange. Some nodes are materialized in space as railway stations, airports, network hubs and, ultimately, cities.

Nodes exist at various sizes and locations. Urban nodes typically consume large swathes of land, due to the open expanse of the infrastructure landscape of platforms, good yards and railways lines(or gates, hangars and run-

Fig.11 Shenzhen Bao’an International Airport, Shenzhen, China

ways), with the enclosed space of a railway station or an airport terminal being relatively minor. In the case of a central railways, the requisite platform configurations splays the modal lines of the rail with the resulting ‘reserve bottlenecking’ having dire urban consequence; large physical present, little social interactivity.

If the growth of a node accelerates, if its mobile programmes and infrastructure amass and

Fig.12

economically accumulate at mega scales, a city agglomerates as a ‘transmetropolis’.

Fig.13

Mobility, in the contemporary context, is a complex concept, ideologically elusive, difficult to pin down. Mobility is a transitory, transformation state, reconfigurable and self-refreshing, time after time. Mobility is an â&#x20AC;&#x2DC;event-spaceâ&#x20AC;&#x2122;, a sequence of appointments and rendezvous. Mobility is multidimensional in that it temporally functions beyond the x-y-z limits of Cartesian space. Mobility is polymorphous: its myriad forms include social mobility, automobility, mobile telephony and eco-tourism, to name but a few. Mobility incorporates information technologies and telecommunication triggering a spatial schizophrenia - today you can be in two places at once!

Mobility even though its functionality is linear concept of it is volumetric. Intention is, wherever the coordinates of the destination are, to arrive through the shortest path. However when it is considered how urban mobility operates, destination point is always projected on the ground floor and when arrived to the projected point a vertical infrastructure is connects people to actual destination. No matter how we perceive mobility in nature it is not performed the same way in the built environment, because cities do not only operate in one ground plane. Urban mobility can not be considered as changing the location from point A to B, since it is inclusive about information technologies as well. Being present in a specific location is not the only way to see or understand the qualities of a space anymore. Since information infrastructures changes the meaning of being present it is important that urban mobility to be responsive to the demand of being present in multiple locations much faster. If we consider the way and the speed the information is acquired today, urban mobility is not even sufficient enough for that pace. Moreover the infrastructure of urban mobility is not evolving accordingly to urban pace or population growth. Therefore even though a city as we understand today is designed to function perfectly it is not going to be sufficient in the next 10 years. Therefore design of this infrastructure should be adaptive, responsive to the social and physical needs for allowing urban mobility to function accordingly to the daily routines and demands. Mobility should be about the journey and destination itself as a whole not some collection of arrival points designated by the infrastructure.

MOBILITY THE MAP A “non-place” is what Marc Auge terms the interstitial location between here and there, between two significant points and a meaningful existence. Non-places detached from any locations or social networks and tend to be anonymous - void of particularity - and inert to geographic position - airports, shopping malls etc.

Places thus never disappear completely and, similarly, a non-place is never fully established; a palimpsest in which an elusion age of identity is being enacted. Non-places turn the subject into a passenger, a user, a customer, or listener, identified by name, address, date of birth, passport and pin number.

Places are identified through the quality of interactions people experience in them. However for transportation the space is being used in an ambiguous way. Transportation infrastructure, vehicles or stations all are spaces that people be present in daily basis, however they do not exactly indicate “a place”. The infrastructure as a space is used as a passage which in some cases can not even provide a “journey“. Therefore a function which occupies a lot of space in the city is a “non-place” because it is lacking the identity. However the infrastructure stated on the ground as streets, passages, pavements in a way identifies the city. Today most of the cities can be easily recognized through their urban patterns. Which creates the ambiguity in a way because, while it is reflecting the urban culture on ground plane, by its use or by the role infrastructure puts on people, it is a “non-place”.

Fig.14 The light-box illuminates a map from the city at night.

Fig.15

To roam is to be mobile - able to move freely from one place or position in the environment to another. The essential companion to mobility is orientation, the knowledge of distance and direction in relation to your surroundings, together with the ability to keep track of spatial relationships as they change while you move about. Maps provide the orientation that you can reach where you want to go, or know exactly where it is that you have ended up:

“You have taught me the fear becoming lost… In strange cities I memorise streets and always know exactly where I am. Amid scenes of great splendour, I review the route back to the hotel.” (1 Keillor, Garrison ’95 Theses 95’, Lake Wobegon Days, London: Faber & Faber, 1985, p. 254)

Urban pattern is one of the most unique qualities of the city. While creating a unique identity for each city through its infrastructure, it is also a reflection of the urban culture. The most ancient way of representing these overlapping patterns is mapping the city. Although it is being understood as a manner of way finding, with contemporary representation tools, maps can be created in variety of ways. Core of the idea of mapping is quite the same, just that, it is being used to indicate any kinds of information today. It can be an indicator of soundscapes, or frequency of use at a certain area. However it is used, it tends to unfold a space and visually creates an expression of it more than reflecting its physical qualities. In that sense it is obvious that city maintains multiple layers of data which is not the most visible in the first glance. How ever we all experience it. It is hard to identify every noise that contributes to a soundscape that one experiences regularly, however, when it is unfolded as a map or visualized, it adds a certain character and new perspective to that place.

The experience of infrastructure is not least a question of representation: Is the structure of the system legible for its users? Is it possible to observe its performance? Are actions of other users visible? What about the consequences of the processes involved? As an increasing amount of real-time information about urban infrastructure becomes available, real-time visualization and feedback mechanisms provide ways to address these questions.

MOBILITY THE MAP

Fig.16 Jason Wallis-Johnson, Portadown, 2001

Fig.17 This project was developed at the Spatial Information Design Lab in collaboration with Sarah Williams, Georgia Bullen, Francis Tan and Noa Youse.

Ecosystems, governments, biological cells, markets, and complex adaptive systems in general are characterized by intricate hierarchal arrangements of boundaries and signals. Ecosystems, for example, have highly diverse niches, with smells and visual patterns as signals. Governments have departmental hierarchies, with memoranda as signals. Biological cells have a wealth of membranes, with proteins as signals. Markets have traders and specialists who use by and sell orders as signals. And so it is with other complex adaptive systems.

Communication is essential to perform daily routines and well functioning urban life. In that sense communication can be either through spoken language or very simple signal based interaction. As it is for transportation systems, this simple signals are vital for it to function properly. In each systems there is a level of signal/boundary communication, which creates an attraction or impulse for system to organize itself. While the signal creates this impulse, boundary refines the level of interaction, determines which impulse is related to the system and which is irrelevant. It is important to evaluate this level of communication as a two stages. First is the source of the signal, which can be created by the agent itself, by another agent or it might be coming from the environment. The second is the boundary, which detects the relevant signal and evaluates the system in order to take an action. After those two stages, the relevant action is being taken, which creates a change in the system. Though language use is distributed and diverse, patterns are pervasive, emerging in a manner reminiscent of the way bird flocks and fish schools arise and persist without central control. ... As a niche formation, then, mechanisms of formation and change play an important part in understanding language. Language acquisition depends on steady learning from the sequenced utterances and actions of members in the language community, with ever-increasing complexity in a learnerâ&#x20AC;&#x2122;s ability to produce meaningful utterance sequences.

Language which is a complex way of communication specific to human in that sense is highly dependent on the learning aspects of the agent. Which makes it more complex than signal/boundary based communication. Only filter that language is going through is the consciousness of the agent, which enables it to learn and improvise to create meaningful responses. While language is also dependent on signals and rule sets such as, grammar and alphabet, it is unique in a way it is constantly changing through its interface, the way and where it is used and its combination with other signals. 24

COMMUNICATION

Fig.18

Scanning â&#x20AC;&#x201C; a form of behavior (sniffing, looking, listening, palpating) by which a sensory stimulus is sought or expected, and which is guided by an expectancy of input instead of a future internal state (goal). The search must be broad in the sense of looking everywhere, but narrow in the sense of being specific as to the characteristics of the anticipated stimulus, which implies that the search is guided by memory of past experience, as distinct from a predicted goal or automatic guidance by a set point. â&#x20AC;&#x201C; From Biographical Sketch: W Grey Walter by Walter J Freeman, Encyclopedia of Cognitive Science (2003)

Machine-Machine Communication: Signal-Boundary based communication. Level of complexity is based on cognition Human-Human Communication: Communication based on the language.

Human - Machine Communication: Communication based on language and cognition.

Machine

Human

Machine

Fig.19

Fig.20

Increased social integration of the sensorimotor interface into everyday communication is giving rise to longer and more contextually varied access to cyberspace. The interface enters the social sphere via easier coupling with the body through miniaturization, portability, and wearability.

As it is discussed before learning is a huge part of language development, therefore high level of communication. In parallel, the level of learning is highly dependent on senses. The more input the sensory-motor receptors receive, more experience is acquired. Since the information infrastructure is highly accessible and fast today, it is fair to say that the interaction level people are in, is higher than ever. People can access information and communicate anytime. Even location and time differences do not effect these systems, communication systems are more simultaneous than ever. People are enhancing their senses through augmenting their bodies with information infrastructures in order not to limit the level of input only to their senses, to receive more impulse, to become more social. The medium of communication therefore is changing evolving to a different interface expanded from our skin and senses.

Ways of communication

Echolocation: There are many types of animals that use echolocation including toothed whales and dolphins and even some birds. In bats echolocation is used for night time navigation (though their eyes are just as good as a human’s). When a bat echolocates it uses pulses of sound that it generates from its throat/larynx (sometimes the nose) which are forced out as sound waves into the environment. The sound waves then bounce off of objects and then the “bounced” sounds are returned to the bat’s ear. The time delay in the sound’s release and return allow the bat to measure distance and depth of objects or prey.

Every action that body can perform depends at some to communication. Inputs received from eyes are signal to the brain therefore, places are recognized, therefore, simple behaviours such as way-finding is obstacle-avoiding is reliant on communication. This process, simple behaviour generation is identified as scanning by Grey Walter, can be seen in many forms in animals as echolocations or pheromone tracking in social insects.

COMMUNICATION Interface can be anything which allows us to interact with our environment. It can be our skin or the devices, cyberspace we use. It almost functions like a boundary that evaluates the related information for us to continue the procedure started with an impulse. First our boundaries were only sensitive as our sensory-motor receptors. Today it expands as far as our information infrastructure.

Recently Facebook had an experiment for artificially intelligent bots to perform simple trading tasks. In the process two bots, manipulated the English language and came up with shortcuts to make the process easier for themselves. Eventually, the language they use became completely different and unique to them and Facebook shut down the experiment. When the language they use investigated they found some rule sets they were using in their own language. In a weird way they were able to conclude the tasks they are given, however the speech was completely unknown to developers. After some investigation it is understood that they were relating how many times they use the word with the amount of desired object.

Fig.21

Fig.22

Fig.23

The design goal is nearly always underspecified and â&#x20AC;&#x153;the controllerâ&#x20AC;? is no longer the authoritarian apparatus which this purely technical name commonly brings to mind. In contrast the controller is an odd mixture of catalyst, crutch, memory and arbiter. These, I believe, are dispositions a designer should bring to bear upon his work (when he professionally plays the part of a controller) and these are the qualities he should embed in the systems (control systems) which he designs. - Gordon Pask

Electronic System An Electronic System is a physical interconnection of components, or parts, that gathers various amounts of information together.

With the aid of input devices such as sensors, input information can be processed by using electrical energy in the form of an output action to control a physical process or perform some type of mathematical operation on the signal. Control System A control system is required to control, manage or regulate a system in order to optimize or make it function properly. It can be a simple temperature control system in a domestic environment or an international transportation optimization, it manages many systems around us to work. In some cases, these systems function without even a human intervention. Open-loop Control System This type of control system is basically functions over given direction. There is no automation, feed-back loops or output evaluation in the control system, therefore output quality is dependent on user experience. Clothes dryer can be an example of open-loop control system. Depending upon the amount of clothes or how wet they are, a user or operator would set a timer (controller) to say 30 minutes and at the end of the 30 minutes the drier(plant) will automatically stop and turn-off even if the clothes where still wet or damp. It does not monitor or measure the condition of the output signal, which is the dryness of the clothes. Then the accuracy of the drying process, or success of drying the clothes will depend on the experience of the user (operator).

Closed-loop Control System In a closed-loop control system, the control action from the controller is dependent on the desired and actual process variable.

CONTROL SYSTEMS

Electronic System

Open-loop Control System

Closed-loop Control System

A Closed-loop control system, also known as a feedback control system is a control system which uses the concept of an open loop system as its forward path but has one or more feedback loops (hence its name) or paths between its output and its input. The reference to “feedback”, simply means that some portion of the output is returned “back” to the input to form part of the systems excitation.

In the case of closed-loop linear feedback systems, a control loop including sensors, control algorithms, and actuators is arranged in an attempt to regulate a variable at a set-point.

For a system to be able to perform given tasks without any human intervention it is important for it to have a closed-loop control system. In such system communication within is highly important for output optimization and efficiency. A control system can be seen as a boundary which is organizing the input in order to create better output each time. However, in that case, boundary refines itself, every time an output has been created through its feed-back loop. When user demands (which can be thought as an input) and output performance is compared the systems can update faulty parts of the process or adjust the steps of procedure. While such automation and control without intervention is highly beneficial for optimizing, it still requires certain declaration about how system will work. In that case, the type of input and possible outcome is limited, and systems adaptation to different conditions are low. For example a rotating solar panel can increase its efficiency, but it does not know the required energy for the system its sourcing. Therefore each system has an internal communication which allows it to optimize, but nothing is interconnected. Problems are evaluated in a closed loop and when one linkage is faulty it does not matter for the other systems are working efficiently or not, the main goal can not be achieved. In that sense, approaching the matter of control should be understood as inclusive to all systems. While they are self-controlling through internal communication within the system, it is also important for them to be able to receive information about the state of interconnected systems. That can be transportation infrastructure to a vehicle or a user to vehicle, if these infrastructure systems are self-controlled and enhanced with communication features, the urban infrastructure as a whole has the potential to organize itself without any intervention.

CONTROL SYSTEMS

Fig.24

Fig.25

Fig.26

An example of the closed-loop control system is the sunseeker solar system. Sunseeker solar system is an automatic tracker which uses LDR to sense the sunlight. A microcontroller reads the LDR voltage and signals the connected motor which rotates the panel towards the sun. To increase the efficiency of solar panel (plant), the sunlight (input) sensor will feedback the position of the sun and lead the panels change their positions (output).

Intelligence: They (behaviour based robots) are observed to be intelligent - but the source of intelligence is not limited to the computational engine. It also comes from the situation in the world, the signal transformations within the sensors, and the physical coupling of the robot with the world, to the computational engine.

Intelligence, and how to acquire intelligence has been studied by cognitive scientists for 50 years now. While there are different approaches to the subject of creating an artificial intelligence, it is accepted that sensory-motor applications and communication has a great importance for the emergence of complex behaviours. Being able to sense the environment therefore having recognition is the first step of acquiring information. The next step is sharing and multiplying that information. If a situated agent can perform both these, it is fair to say that agent is able to learn. Therefore to be intelligent or to emerge new behaviours and agent needs to learn. Through learning even simplest declared behaviours can lead to complex behaviors which allows for agent to take decisions and perform tasks.

Learning exhibits considerable emergence and trial-error search, and oddly enough, the results of learning can even affect the course of evolution. There is no clean separation between these processes, no straight line of causality.

Learning even if it is not proven his highly related with evolution. For something to evolve. For instance, for a specie to evolve or adapt, the actions they take under same consequences will lead either to success or failure. The ones that are failing will eventually go through natural selection and the actions that are leading to success will be adopted by the next generation to increase survival chance. This basic Darwinist way to see evolution, is indicating that learning is important to generate species that can adapt and take better choices. For a system which is able to adapt and responsive to variety of situations, it is important for that system to learn through its own experiences or by evaluating the input from its environment. That input can be coming from communicating with the systems own agent, user or the control system that it may acquire. It is important for the agent/system to process that information and through time give a fitter response for every situation.

LEARNING

Fig.33

Grey Walter’s Tortoises In two articles published in “Scientific American” he calls his constructions a new genus of “mechanical tortoises” and provides not only the details of their new construction but analysis of their complex behaviour. CORA circuit: Second generation of tortoises with the ability to learn and develop reflexes. In that sense this application was mechanical equivalent of Pavlov’s experiment. Complex intelligence is better understood and more successfully embodied in artifacts by working up from low-level sensorimotor agents than by working down from abstract cognitive mechanisms of rationality such as deduction, induction, and means-ends analysis. ...

Essentially, ALife researchers believe that AI’s holy grail, common sense, comes only via the learned experiences of a body in a world Fig.34

Now, we are self-organizing systems and we wander around in a world which is full of wonderful black boxes, Dr. Ashby’s black boxes. Some of them are turtles; some are turtledoves; some are mocking birds; some of them go”Poop !” and some go “Pop !“; some are computers; this sort of thing. Gordon Pask, “A Proposed Evolutionary Model“ (1962, 229)

Self-organization is the essential way how biological and non-biological world organizes itself. Concept has proven useful in biology, from molecular to ecosystem level. It has been realized in bio-chemical reactions and in the physics of non-equilibrium processes as well. Cited examples of self-organizing behaviour also appear in the literature of many other disciplines, both in the natural sciences and in the social sciences such as economics or anthropology. Self-organization has also been observed in mathematical systems such as cellular automata [Fig.27] which can simulate complex behaviours of living things. Stephen Wolfram, began to investigate cellular automata as a self organizing system. They crated a parallel processing computation system by changing the states of the system, therefore the calculations had to be made simultaneously. In a self-organizational system, multiple agents can form local packs. One of the crucial features of self-organizational system is that does not necessarily need a leader or centralized controller. The system is able to adjust and response through local interactions. However when local packs might perform different behaviours, the coherency is maintained. Self-organization is an agent-based system which contains a decentralized intelligence that operates on local rules. Although single agent is not capable of performing a task, collectively the system can achieve complex behaviours. Through communication system can overcome complex tasks such as food finding or building ant bridges. [Fig.28]

Fig.27

SELF-ORGANIZATION Self-organization can be described on four basic concepts:

Positive feedback: they are simple behavioural rules that promote the creation of structures. For instance recruitment to a food source is a positive feedback that relies on trail laying and trail following in some ant species, or dances in bees. Negative feedback: Counterbalances positive feedback and helps to stabilize the collective pattern. It may take the form of saturation, exhaustion, or competition. Fluctuations: (Random walks, errors, random task-switching, and so on) Not only do structures emerge despite randomness, but randomness is often crucial, since it enables the discovery of new solutions, and fluctuations can act as seeds from which structures nucleate and grow. Interactions: A single individual can generate a self-organized structure such as a stable trail provided pheromonal lifetime is sufficient, because trail-following events can then interact with trail-laying actions.

Fig.28

A social insect colony is undoubtedly a decentralized problem-solving system, comprised of many relatively simple interacting entities. The daily problems solved by a colony include finding food, building or extending a nest, efficiently dividing labor among individuals, efficiently feeding the brood, responding to external challenges, spreading alarm, etc. Many of these problems have counterparts in engineering and computer science. One of the most important features of social insects is that they can solve these problems in a very flexible and robust way: flexibility allows adaptation to changing environments, while robustness endows the colony with the ability to function even though some individuals may fail to perform their tasks.

Fig.29

Self-organizing systems are adaptive and robust. They can reconfigure themselves to changing demands and thus keep on functioning in spite of perturbations. Because of this, self-organization has been used as a paradigm to design adaptive and robust artificial systems. The main idea is to engineer elements of a system so that they find a solution or perform a desired function. This approach is useful in non-stationary or very large problem domains, where the solution is not fixed or is unknown. Thus, the engineer does not need to reach a solution, as this is sought for constantly by the self-organizing elements.

Fig.30

SELF-ORGANIZATION

Self-organization lies in attractivity, which induces a snowball effect: the larger a cluster, the more likely it is to attract even more items. But self-organization can also be combined with a template mechanism in the process of clustering. [Fig.29]

Self-organization is one of the driving force for he proposed system since architecture is seen as system that is able to response at city scale simultaneously. While material and structural behaviours of single agents are more simplistic and designed to perform simple tasks, a self-organized collection of agents are capable of achieving higher goals. Their performative qualities as a pack, are designed to structure and organize on urban scale. Through their ability to understand the environment requirements and evaluate the inputs with user demands, they are able to organize the city through its infrastructure.

Absence of centralized coordination is a defining feature to self-organization. Instead organization emerges from local interactions only. Instead of looking for an externally imposed pattern, it searched for internally evolved structures of organization.[Fig.30] No predefined design pattern but a result of the internal interaction between the elementary parts and often resulting from only simple rules of interaction.

The reason urban systems are considered as an interactive infrastructure, the level of mobility city requires today is highly dependent on the quality and organization of its own infrastructure. Today, it is impossible to consider such an informatics based system being controlled by a centralized system. The dynamics of urban life, routines and demands are constantly changing. Therefore, if such interconnected infrastructure system is going to provide mobility for urban life it should be able to evaluate the current and local trends and take decisions accordingly. In that sense, it is important for the system proposed to be able to self-organize in, local and global scale. No declared functional pattern will provide such flexibility.

Self- organization: The system organizes itself, but there is no â&#x20AC;&#x153;selfâ&#x20AC;?, no agent inside the system doing the organizing.

Fig.31

Fig.32

Metomorphic robotic systems: A collection of independently controlled mechatronic modules, each of which hast the ability to connect, disconnect, and climb over adjacent modules. [Fig.31] All modules have the same physical structure (homogenity), and each module is autonomous from the viewpoint of computation and communication. The modules are introduces as two types hexagonal and square. [Fig.32] However compared to square modules hexagonal ones had more joint reconfigurations therefore it profided more flexibility and variety of assembly to the system.

SELF-ORGANIZATION The agents are diverse rather than standardized, and both their behaviour and their structure change as they interact

A system which is able to evaluate local conditions and act accordingly will not have the same experiences. Therefore it might create different behaviour patterns among the system. Which is beneficial for the system to serve accordingly to the local demands and benefits. As the differentiated agents interact with each other while performing variety of tasks, the way they structure and organize will change. Such diversity is also important to optimize and increase the efficiency of the system so it can understand different existing patterns and provide variety of new patterns that fit the demand and requirements.

A synthesis of material and computational interaction constructs a generative organisation of space and structure that explores a behaviour-based model of living through patterns found in nature. ... System-to-system interactions identified through simple rule-based protocols can collectively exhibit complex non-linear behaviour. The magnitude of these interactions is explored across varied scales to test the potential of self-structuring orders constructed through the inter play of local agency and environmental stimulus.

Such diversity and level of organization in the system can be achieved by designing agents that are able to communicate with each other and the user, recognize its environment and its user and additionally able to learn through experience and shared information. When such interconnectivity is achieved among the agents they can perform infinite amount of tasks among the city while they are learning local patterns and requirements. Simple local evaluations and adaptations can feed the whole urban system to achieve an responsive, evolving urban infrastructure. Not being dependent on a designated infrastructure higher mobility can be achieved. With such adaptive infrastructure, mobility is going to have the potential to change according to informatics, represent the hidden layers of the city and create a reflection of urban culture. The city is not going to be planned from top view anymore. Users are going to claim their right to be able to take decisions on urban design and contribute through their mobility.

Hobermann Sphere

BEHAVIOUR

After defining and investigating behaviours of a single agent we are increasing the number of protocells to construct local packing which will have different levels of behaviours in our system. Through the simple behaviours of a single protocell we want to construct higher level of organization within a local pack. Purely depending on the interaction, communication and collaboration of multiple protocells, local pack is able to achieve higher organizational skills. 42

BEHAVIOUR CONNECTIVITY

FIXED JOINT

FLEXIBLE JOINT

Single Layer Surface

Double Layer Surface

Rotation achieved through moving magnet on outer layer

Magnets are strategically placed on the outer layer to achieve position change

CONNECTIVITY joined types

As an another example to be able to expand and contract we experimented with springs and expandable components. To investigate more with deformation we tested the model pressure and stretching forces. As you can see in the videos and pictures the model is not able to protect its deformed shape.

Fixed Joint Connectivity

BEHAVIOUR CONNECTIVITY

As an another example to be able to expand and contract it is experimented with springs and expandable components. To investigate more with deformation it is tested the model pressure and stretching forces. Because of the way the model constructed, this proposed design is not able to keep its deformed shape.

Fixed Joint Connectivity

BEHAVIOUR CONNECTIVITY As an another example for prototype to be able to expand and contract, it is experimented with springs and expandable components. To investigate more with performative structures, tests are made with the model by pressurizing and stretching. As it can be seen in the videos and pictures the model is not able to protect its deformed shape.

For us next step is to achieve an organizational model with higher population. Therefore experiments are made with different number of connections in a specific pattern. Although different types of movement is required, lacking reconfigurability in the joints while trying to achieve that variety, results with decrease in reorganization qualities in the system. As it is expected from the system to create local packs to achieve targets it is important to identify how connectivity is achieved in the system. To have the flexibility that system requires we decided that it is useful for us to have two different joint types. In one case, is fixed and all the other behaviors occur dependent on the joint, the other is flexible which allow agents to adjust their connection.

BEHAVIOUR CONNECTIVITY

For the system development, next step is to achieve an organizational model with higher population. Therefore experimentations are made with different number of connections in specific pattern. Although observations indicated different type of movements can be acquired, lacking reconfigurability of the joints the system losses the ability reorganize itself.

BEHAVIOUR CONNECTIVITY

The results achieved through pre-defined patterns. While less connections resulted with less dependency to the neighbors, therefore less control over the system, more connections result with densely packed less mobile system.

Electromagnet Array

BEHAVIOUR CONNECTIVITY To further experiment with magnets some tests are made with electromagnet arrays. By selectively activating the magnets one by one it is achieved for this system to change its state. However to be able to make one sphere to change slots in the array activation and deactivation is not enough to achieve mobility. That kind of control requires reverse currency to control to push from one slot while pulling to the other slot.

Since th environment input is a key feature for our systems dynamism, it is important to define a recognition system which will allow protocells to have their own cognition. To decide the recognition system, we experimented with couple of control systems like sound or language recognition systems. However controlling the agents with specific dictionary is over directed and can create unbiased situations for the system. The goal of the system is to be able to improvise through recognised patterns in the environment. To achieve that it was not beneficial to use command based control systems. The system through its recognition system, should be able to understand and analyze in order to perform a behavior. Therefore it is decided that it is beneficial to enhance the system with sonar recognition and signal based communication.

Sonar Mapping

BEHAVIOUR RECOGNITION

Sonar Mapping The experiments made after the aspect of mobility proved the point the performance of the system is highly dependept to the recognition of the exisiting patterns in the environment. In order to achieve such level of recognition, a sonar mapping system is designed. While it is not executing an exact model of the environment, it allows protocells to identify objects and understand how far they are. The system is tested with different materials in order to understand how it responds to the material. It revealed that while system is able to recognize an obstacle, it maps each material with different density of point clouds. Therefore, the system has the potential to recognize even materials.

Environment Test 1 Material Distance X Axis Dimension Y Axis Dimension Z Axis Dimension

: Foam : 30 cm : 50 cm : 70 cm : 55 cm

Foam emits the sound waves and reduces the density of points

Environment Test 2 Material Distance X Axis Dimension Y Axis Dimension Z Axis Dimension

: Plexi : 25 cm : 20 cm : 60 cm : 55 cm

Plexi is a reflective material so sensor is able to detect the surface of the object clearly

Environment Test 3 Material Distance X Axis Dimension Y Axis Dimension Z Axis Dimension

BEHAVIOUR RECOGNITION : Metal : 30 cm : 50 cm : 70 cm : 55 cm

Metal is a reflective material so sensor is able to detect the surface of the object clearly

Environment Test 4 Material Distance X Axis Dimension Y Axis Dimension Z Axis Dimension

: Clay : 25 cm : 20 cm : 60 cm : 55 cm

Clay emits the sound waves and also jeopardizes the clarity of environment as well

Environment Test 5 Material 1 Material 2 Material 3 Distance X Axis Dimension Y Axis Dimension Z Axis Dimension

: Balloon : Clay : Metal : 30 cm : 50 cm : 70 cm : 55 cm

Clay is more readable with other materials in the same environment

BEHAVIOUR RECOGNITION

Sonar Mapping with Sonar Sensor, Processing and Arduino Uno

MACHINE LEARNING

Since we defined environment input as a key feature to our systems dynamism, it is important for us to define a recognition system which will allow our agent to have their own cognition. To decide the recognition system, we experimented with couple of stuff also language based systems. However controlling the agents with specific dictionary is over controlled and can create unbiased situations for the system, because it is dependent on individual control. We wanted our system to be able to understand and analyze to perform a behavior. In order to achieve that we decided to enhance our system with sonar recognition and signal based communication.

There some variations of how to define the types of Machine Learning Algorithms but commonly they can be divided into categories according to their purpose and the main categories are the following:

SUPERVISED LEARNING The computer is presented with example inputs and their desired outputs (labeled data), given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs. As special cases, the input signal can be only partially available, or restricted to special feedback. Labeled data: Data consisting of a set of training examples, where each example is a pair consisting of an input and a desired output value (also called the supervisory signal, labels, etc)

UNSUPERVISED LEARNING No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). Here there’s no teacher at all, actually the computer might be able to teach you new things after it learns patterns in data, these algorithms a particularly useful in cases where the human expert doesn’t know what to look for in the data.

MACHINE LEARNING TYPES OF MACHINE LEARNING

SEMI-SUPERVISED LEARNING The computer is given only an incomplete training signal: a training set with some (often many) of the target outputs missing. In the absence of labels in the majority of the observations but present in few, semi-supervised algorithms are the best candidates for the model building.

REINFORCEMENT LEARNING Training data (in form of rewards and punishments) is given only as feedback to the programâ&#x20AC;&#x2122;s actions in a dynamic environment, such as driving a vehicle or playing a game against an opponent. The method aims at using observations gathered from the interaction with the environment to take actions that would maximize the reward or minimize the risk. Reinforcement learning algorithm (called the agent) continuously learns from the environment in an iterative fashion. In the process, the agent learns from its experiences of the environment until it explores the full range of possible states

Powered by Unity, the cross-platform game engine, we are able to be at the crossroads between machine learning and gaming. With the ML-Agents software development kit, which allows researchers to transform games and simulations into environment where intelligent agents can be trained by machine learning algorithm, we can conduct the experiments of training intelligent and self-adaptive architectural system.

(Machine Learning Library by Google) A Possibility of Configuration of Learning Environment within Unity ML-Agents

Agent

Each Agent can have a unique set of states and observations, take unique actions within the environment, and receive unique rewards for events within the environment. An agent’s actions are decided by the brain it is linked to.

Brain

Each Brain defines a specific state and action space, and is responsible for deciding which actions each of its linked agents will take. The current release supports Brains being set to one of four modes: External – Action decisions are made using TensorFlow (or your ML library of choice) through communication over an open socket with the Python API. Internal – Actions decisions are made using a trained model embedded into the project via TensorFlowSharp. Player – Action decisions are made using player input. Heuristic – Action decisions are made using hand-coded behavior.

Academy

The Academy object within a scene also contains as children all Brains within the environment. Each environment contains a single Academy which defines the scope of the environment. The states and observations of all agents with brains set to External are collected by the External Communicator, and communicated to our Python API for processing using the ML library of choice (TensorFlow). By setting multiple agents to a single brain, actions can be decided in a batch fashion, opening the possibility of getting the advantages of parallel computation, when supported.

MACHINE LEARNING APPROACH The ML-Agents SDK enables us to use Reinforcement Learning to conduct the machine learning experiment. As mentioned above, the reinforcement learning using the relationship between agents and environment to acquire a classified data or a learned behaviour patterns of agents within a task: The method aims at using observations gathered from the interaction with the environment to take actions that would maximize the reward or minimize the risk. Reinforcement learning algorithm (called the agent) continuously learns from the environment in an iterative fashion. In the process, the agent learns from its experiences of the environment until it explores the full range of possible states

Agent Properties within Unity ML-Agents

State Space

Action Space

Brain

A set of minimal variables that can define the state of agent.

A set of actions that the agent can take.

Each Brain defines a specific state and action space, and is responsible for deciding which actions each of its linked agents will take.

Reinforcement Learning Reward Driven Optimization Mechanism

- The agent have an observation into the environment - The agent takes action -The state of environment and agent itself change -Giving or reducing rewards to the agent based on rule set -Optimizing behaviours to acquire more rewards

Single Agent Learning Agent: Single sphere Goal:

Occupying as much volume as it can within the cube

State Space: 4 (X, Y, Z, Radius) Action Space: 6 (X+, X-, Y+, Y-, Z+, Z- ) radius decided by the closest distance between the center and boundaries Volume

Environment:

Cube

Observation:

Position within the cube

X: Position on X component Y: Position on X component Z: Position on X component V: Cube Volume â&#x20AC;&#x201C; Sphere Volume S: Time of success( v < 0.5)

Reward Criteria: Out of the cube: rewardVolume increase: reward+

After 10min Learning

MACHINE LEARNING EXPERIMETS Multi Agents Learning Within reinforcement learning for multi agents in the ML-Agents environment, the relationship between agents are mainly decided by their brains and reward sets. Brains of agents decide the learning of actions and how agents observe the environment, which means their learned bahaviour pattern, while the rewards configuration set by the developer determine the actual goal of each agent within the specific task. In this case, we can share or separate the brains and reward sets of multi agents and that is the way we change the relationship between agents (cooperative and competitive), and their goals.

Agents with the Shared Brain & Shared Rewards

Agents with the Separated Brains & Shared Rewards

Agents with the Shared Brain & Separated Rewards

Agents with the Separated Brains & Separated Rewards

Agents

Shared Rewards

Separated Rewards

Shared Brain

Achieving cooperating goal with same behavior pattern

Seperated Brains

Achieving cooperating goal with same behavior pattern

Competitive Agents Learning Goal:

Each sphere try to get bigger than each other

Reward Criteria: Sphere A volume is larger Sphere A reward + Sphere B volume is larger Sphere B reward + Agent:

Two Spheres

Brain:

Shared Brain

State Space:

4 (X, Y, Z, Radius)

Action Space:

8 (X+, X-, Y+, Y-, Z+, Z-, R+, R-)

Environment:

Cube

Observation:

Position within the cube and to other spheres

Volume -

X: Position on X component Y: Position on X component Z: Position on X component R: Radius S: Time of success (Both attached to boundaries and each other)

After 1 Hour Learning As the competitive relationship between agents, the bigger agent get more rewards.

MACHINE LEARNING EXPERIMETS

Cooperative Agents Learning

Goal:

Higher total volume within the cube

Reward Criteria: Total volume increase: Sphere A & Sphere B reward + Agent:

Two Spheres

Brain:

Shared Brain

State Space:

4 (X, Y, Z, Radius)

Action Space:

8 (X+, X-, Y+, Y-, Z+, Z-, R+, R-)

Environment:

Cube

Observation:

Position within the cube and to other spheres

Volume -

X: Position on X component Y: Position on X component Z: Position on X component R: Radius S: Time of success (Both attached to boundaries and each other)

After 1 Hour Learning As the cooperative relationship between agents, both of them have same rewards with larger total volume.

Agent Behaviour Learning

Project: OwO, Theodore Spyropoulos Studio, 2015

Twisting

Crawling Combination of units and movement

MACHINE LEARNING EXPERIMETS

As machine Learning can process and find rules of complex and indistinct data, we can also use it to explore agentsâ&#x20AC;&#x2122; complex behaviour. Behaviours of the agent can be divided into two types, which are low level behaviours and high level behaviours. The low level behaviours represent those behaviours that is local, simple and within a single unit, and through the assembly of multi units with different configurations and the combination low level behaviours of assembled units, they can acheive high level behaviour which can be totally different. However, the combination of behaviours and how they perform are indistinct so we can use machine learning to explore the mechanism of the behaviours combination to achieve efficient high level behaviours.

Agent Behaviour Learning - Mobility of 2 Spheres

Low Level Behaviour: (Single Directional Strectch)

Agent Body Configuration:

(Fixed Joint)

Learning Task: Reach the fixed target (Each Agent are trained individually)

MACHINE LEARNING EXPERIMETS

Before Learning Agents were taking random actions (stretches) and bouncing to both direction without known targets.

After 30 minutes Learning

Agents started knowing the right direction to the target and having unstable movement forward.

After 2 hours Learning

Agents found stable action pattern toward targets while they are not efficient enough.

After 5 hours Learning

Agent learned new pattern of action which is rolling-bouncing and they are able to reach targets efficiently.

Agent Behaviour Learning - Mobility of 4 Spheres

Low Level Behaviour: (Sphere Expanding)

Agent Body Configuration: (Spring Joints)

Learning Task: Reach the target changing positions (Each Agent are trained individually)

MACHINE LEARNING EXPERIMETS

Before Learning Agents were taking random actions (changing scales) without any obvious movement.

After 30 minutes Learning Agents started exploring action patterns even though there was still no obvious movement.

After 2 hours Learning Agents found specificly action patterns to move toward target slowly.

After 5 hours Learning Agents found a stable and efficient action pattern to reach the target rapidly.

Expandable StructureHoberman Sphere

MATERIALITY

Pattern 1

PATTERN UNDER NO PRESSURE

Pattern 2

PATTERN UNDER NO PRESSURE

Pattern 3

PATTERN UNDER NO PRESSURE 80

MATERIALITY PNEUMATIC MODELS

PATTERN UNDER HIGH PRESSURE

Since our behavior outline is identified it is also important to experiment about how we can achieve that kind of complexity. Since one of our most important quality is to be able to expand and contract we experiment with different ways and materials to achieve that flexibility. In this experiment we tried to pattern the sphere in order to achieve deformation. For this experiment we evaluated being able to expand and shrink as not a uniform behavior but as being performative.

PATTERN PRODUCTION - NEGATIVE MOLDING - FOLDING

CONSTRUCTING DEFORMABLE GEOMETRY

MATERIALITY ORIGAMI FOLDING

CONSTRUCTING TOPOLOGY

PERFORMATIVE UNIT

MATERIALITY ORIGAMI FOLDING

The 3D Copypod Project using Hoberman Sphere by Peopleâ&#x20AC;&#x2122;s Industrial Design Office https://www.engadget.com/2017/07/05/the-big-picture-copypod-3d/ 87

Since we are researching into the expandable unit, a structure that enables itself to expand and shrink can be a suitable solution for the protoype. A Hoberman sphere is an isokinetic structure patented by Chuck Hoberman that resembles a geodesic dome, but is capable of folding down to a fraction of its normal size by the scissor-like action of its joints. As we figure out the mechanism of the system, we will be able to utilize it to the implement of intelligent agent behaviours which will be more complex.

Structure Configuration

Six basic point on coordinates

Components positions and forming the sphere

Connection component positions and types

Scissor-like Structure

As more scissors are added, ratio of folded to unfolded size decreases. Higher complexity of component means greater packing efficiency.

MATERIALITY SURFACE GEOMETRY

Surface Tessellation

Surface Vectors

Expandable Structure

Scissor numbers & sphere contraction effect Scissor number: 12

Scissor number: 16

Contraction rate: 1.00

Contraction rate: 0.81

Contraction rate: 0.74

Contraction rate: 0.50

Contraction rate: 0.38

MATERIALITY ORIGAMI FOLDING

Scissor number: 20

Scissor number: 32

Contraction rate: 1.00

Contraction rate: 0.70

Contraction rate: 0.63

Contraction rate: 0.31

Contraction rate: 0.19

More scissor components, higher contraction rate (radius changing)

Organizational Model

Ferrofluid Experimet FERROFLUID

SELF-ORGANIZATION

Packing as a system, refers more optimization rather than self-organization. However, when agents which are packing have behavior, the system, reorganizes itself as a result of negotiation for space. With this logic we started to experiment how different types of negotiation may result with different packing systems. By adding simple behaviors to a sphere packing system we experimented how space can be seen as a medium of negotiation. In this specific model, space is represented as a cube and agents have to expand to the limits of the space. However a specific sphere, requires more space, therefore they need to negotiate for space and arrange their expansion rate to achieve stabilized condition.

SELF-ORGANIZATION PACKING

When behavioral complexity added to the system, negotiation for space and occupation of space are drastically changing. In this case, two different types of agents, which have different behavioral qualities are fighting for space. While one set is more trying to stay together, others are more individual in the space. Therefore, the system constantly changes its state until it reaches to stability. Which also results with domination in the space. However in the system, the stabilization always results with fully packed environment. Which opens up the conversation about environment and how some input from the environment might affect the state of stabilization process by increasing the likelihood of the system to reorganize accordingly to the environment input.

Repulsion to other family

Attraction within the family

Family 1 Attraction within Family : Low Repulsion to other Family: High

Family 2 Attraction within Family : High Repulsion to other Family: Low

SELF-ORGANIZATION PACKING

Local Packing To achieve a target with higher population we specified 3 types of behavior. Local packs have the ability to identify a target and send signals to individuals. If local pack is in the range of an individual they join the pack to achieve the target.

Individual behavior

Random Walk

Cluster Search

Target Seeking

SELF-ORGANIZATION LOCAL PACKING

Organizational Packing

To operate in real world situation when they receive a signal, protocells needs to self-organize from distributed state to the state of being fully packed. Protocells within the radius of signal will search for closest agents to form clusters. After clusters move towards to target to become fully packed.

1 - Distributed

2 - Target Signal

3 - Distance Check

4 - Local Packing

5 - Full Packing

100

SELF-ORGANIZATION ORGANIZATIONAL PACKING

Reaching Target

After protocells formed local packs on ground level to reach a target up in z axis we are using centroid and gradient. Centroid represents the densest point of the pack. According to distance to the centroid agents are labeled with different colors that represents their behavior to achieve a target up on the z axis.

Pack Target

Centroid Target

Gradient

101

Local Packing

Distributed

Full Packing

Gradient

Centroid

Labeling

102

Gradient

Target Reached

SELF-ORGANIZATION ORGANIZATIONAL PACKING

Local Packing

Distributed

Full Packing

Centroid

Gradient

Labeling

103

Gradient

Target Reached

Local Packing

Distributed

Full Packing

Centroid

Gradient

Labeling

Target Reached

104

SELF-ORGANIZATION ORGANIZATIONAL PACKING

105

Reaction-Diffusion

SELF-ORGANIZATION REACTION DIFFUSION

Reaction Diffusion Systems

Reaction–diffusion systems are mathematical models which correspond to several physical phenomena: the most common is the change in space and time of the concentration of one or more chemical substances: local chemical reactions in which the substances are transformed into each other, and diffusion which causes the substances to spread out over a surface in space. Reaction–diffusion systems are naturally applied in chemistry. However, the system can also describe dynamical processes of non-chemical nature. Examples are found in biology, geology and physics and ecology. Mathematically, reaction–diffusion systems take the form of semi-linear parabolic partial differential equations. The system primarily relies on local interactions of chemicals and diffusion Therefore we will use it as a tool to generate self-organized protocells at city scale.

Chemical A is added at a given “Feed” rate

Reaction Bs converts A into B B reproduces as A is consumed

Diffusion Both chemicals diffuse so uneven concentrations spread out across the grid, but A diffuses faster than B

108

Chemical B is removed at a given “Kill” rate

SELF-ORGANIZATION REACTION-DIFFUSION SYSTEMS

The system is approximated by using two numbers at each grid cell for the local concentrations of A and B By checking all neighbors diffusion rate is recalculated with different weights corresponding the reaction rate System is able to create such patterns with simple and local rules

When a grid of thousands of cells is simulated, larger scale patterns can emerge. By applying different kill, feed and diffusion rates it is possible to achieve different patterns as seen below.

109

Same rates with different seeds

Seed

Rules Diffusion U Diffusion V Feed Kill

1.0 0.3 0.054 0.062

Seed

Rules Diffusion U Diffusion V Feed Kill

110

1.0 0.3 0.054 0.062

SELF-ORGANIZATION REACTION-DIFFUSION SYSTEMS Seed

Rules Diffusion U Diffusion V Feed Kill

1.0 0.3 0.054 0.062

Seed

Rules Diffusion U Diffusion V Feed Kill

111

1.0 0.3 0.054 0.062

Same seed with different rates

Seed

Rules Diffusion U Diffusion V Feed Kill

1.0 0.3 0.054 0.062

Seed

Rules Diffusion U 1.0 Diffusion V 0.3 Feed 0.045-0.070 Kill 0.01-0.08

112

SELF-ORGANIZATION REACTION-DIFFUSION SYSTEMS Seed

Rules Diffusion U 1.0 Diffusion V 0.3 Feed 0.054 Kill 0.067-0.065-0.06

Seed

Rules Diffusion U 1.0 Diffusion V 0.1-0.3-0.5 Feed 0.054 Kill 0.062

113

Same seed with different rates

Seed

Rules Diffusion U 1.0 Diffusion V 0.1 - 0.3 - 0.5 Feed 0.054 Kill 0.062

Seed

Rules Diffusion U Diffusion V Feed Kill

114

1.0 - 0.9 0.2 - 0.7 0.054 0.062

SELF-ORGANIZATION ORGANIZATIONAL MODEL

Type 1

Different Conditions

Higher Population of Black Small Population of White

1. Diffusion (Transaction) Speed of Populations 2. Feed and Kill Rates 3. Additional Forces Gravity/Rotation 4. Regionalization Conditional Gradient

Type 2 Higher Population of Black Black if Fed by White

We decided that we need input from the environment to start investigating about reaction diffusion systems on the level of individual protocells. Reaction diffusion systems are self-organizing systems which reaches to stabilization depending on the environment conditions. So whenever the conditions change, systems has to reorganize and agents have to negotiate for space accordingly to the changed states of environment. In our experiments we used a reaction diffusion set up which will simulate some kind of change in environment conditions. Therefore as we foresee whenever system stabilizes, we are changing some condition in our computational environment to simulate the reorganization.

115

SELF-ORGANIZATION FERROFLUID EXPERIMETS

Ferrofluid is a liquid with the attribute to react to magnetism. When placed within the influence of a magnetic field, the fluid starts to grow conical volumes, similar to spikes, like a blowfish. These spikes are repelling each other, thus making ferrofluid change shapes constantly. The image in the left is taken at the moment that the magnet is further away from the liquid than on the image in the right. When the magnet is taken away, ferrofluid retakes its fluid condition.

Light Polarization Using Ferrofluids and Magnetic Fields

The magnetic field changes the structure of the fluid, causing it to form rows of nanoparticles in the direction of the magnetic field. This visual effect caused by magnetism, a magneto-optic effect, can be seen under polarized light.

SELF-ORGANIZATION

120

SELF-ORGANIZATION

As we decided that we need input from the environment we started to investigate about reaction diffusion systems. Reaction diffusion systems are self-organizing systems which reaches to stabilization depending on the environment conditions. So whenever the conditions change, systems has to reorganize and agents have to negotiate for space accordingly to the changed states of environment. In our experiments we used a reaction diffusion set up which will simulate some kind of change in environment conditions. Therefore as we foresee whenever system stabilizes, we are changing some condition in our computational environment to simulate the reorganization.

121

ARCHITECTURAL PROPOSAL DENSITY MAP

DENSE - SPARSE ROUTE STARTING POINT

124

ARCHITECTURAL PROPOSAL CITY SCALE ORGANIZATION

125

DENSE - SPARSE ROUTE STARTING POINT

126

ARCHITECTURAL PROPOSAL CITY SCALE ORGANIZATION

127

128

ARCHITECTURAL SELF-ORGANIZATION PROPOSAL AERIAL PERSPECTIVE

129

BIBLIOGRAPHY

1. Bonabeau, Eric. “Swarm Intelligence: From natural to artificial system.”, 1999. 2. Johnson, Steven. “Interface Culture: How new technology transforms the way we create and communicate”, 1997. 3. Mitchell, William J. (William John). “City of Bits: space, place, and the infobahn”, 1995. 4. Holland, John H. “Signals and boundaries: building blocks for complex adaptive systems”, 2012. 5. Pickering, Andrew. “The cybernetic brain: Sketches of another future”, 2010. 6. Johnston, John. “Allure of Machinic life: Cybernetics, artificial life and the new AI”, 2008. 7. Finizio, Gino. “Architecture & Mobility: tradition and innovation”, 2007. 8. Spiller, Neil. “Cyber_reader: Critical writings for the digital era”, 2002. 9. Spyropoulos, Theodore. “Adaptive ecologies: Correlated systems of living”, 2011. 10. Negroponte, Nicholas. “Being digital”, 1995. 11. Offenhuber, Dietmar. Schechtner, Katja. “Inscribing a square: Urban data as public space”, 2013. 12. Houben, Francine. A room with a view, 2003. 13. Hoete, Anthony. Roam : reader on the aesthetics of mobility, 2003. 14. http://www.karlsims.com/rd.html 15. https://blogs.unity3d.com/2017/09/19/introducing-unity-machine-learning-agents/ 16. https://en.wikipedia.org/wiki/Self-organization 17. https://en.wikipedia.org/wiki/Control_system

131