4057 AADRL Research project

4057

Architectural Association School of Architecture AA School Design Research Laboratory

4057

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

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

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AA DRL

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Design Research Laboratory Architecture and Urbanism Experimentation and Innovation (v.20) The Design Research Laboratory (DRL) is a 16-month post-professional design research programme leading to a Master of Architecture and Urbanism (MArch) degree. The world-renowned lab has been at the forefront of design experimentation for the last twenty years, pioneering advanced methods in design, computation and manufacturing. Beyond representation the lab is an evolving framework exploring three-year research cycles that interrogate architecture and urbanism from the city scale to the nano-scale. Led by leaders in the filed of architecture, design and engineering the AADRL pursues innovation and interdisciplinary design that has also been recognized in many fields outside of architecture fostering collaborations with companies the likes of Ferrari, Festo, AKTII, Reider and Odico Robotics. The lab remains a space of collaboration, curiosity and space and looks to develop the generation of architects that actively participate and influence the field. Distinguished graduates have gone on to found world-renowned offices, lead advanced research groups or have become educators.

DRL studio projects begin in January each year with the formation of design teams that carry forward discoveries made in the Phase I workshops and seminars. The design research work presented by the ten teams in this two-day public jury that takes place in the AA Lecture Hall concludes Phase II of the Design Research Laboratory’s MArch programme.

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AGENDA

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Constructing Agency (v.2)

Our current agenda, Constructing Agency, explores expanded relationships of architecture by considering the future of living, work and culture. The aim of the research is to expand the field of possibilities through exploiting behavior as a conceptual tool in order to synthesize the digital world with the material world. Advanced computational development is utilized in the pursuit of architectural systems that are adaptive, generative and behavioural. Using the latest in advanced printing, making and transformative, kinetic and robotic are all part of the AADRL agenda with the aim to expand the discipline and push the limits of design within the larger cultural and technological realm.

The three studios are: Future Culture Theodore Spyropoulos’ studio, explores how behaviour-based design methods can be used to reconsider cultural projects for today through the development of selfaware and self-structuring practices that see architecture as an infrastructure to address latency and change.

Future Work Patrik Schumacher’s studio entitled, Agent-based Parametric Semiology, aims to contribute to the ‘semiological project’ that promises to upgrade architecture’s communicative capacity within work environments and thus the social functionality of the designed/built environment through designed architectural code that manifests itself via crowd modelling of the agent’s behavioural rules.

Future Living Shajay Booshan’s studio, House.Ocuupant.Science.Tech.Data (HOSTD), explores robotic fabrication while enabling mass customization strategies that can compete with contemporary co-living models within highly productive cities. The promise of mass customization integrated with the new models of housing now allows for generation of a vibrant community fabric.

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STUDIO AGENDA

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Spyropoulos Studio Future Culture 2.0 London What once served to categorise the natural and the man-made have been rendered obsolete. When distinctions of the real and artificial, human and non-human fail to offer insight, the challenge is how best can we design for a latent and unknown world. Within this uncertainty, new conceptual terrains emerge that raise questions of agency and intelligence. Hans Hollien’s provocative 1968 contribution in the journal Bau titled ‘Alles ist Architektur(Everything is Architecture)” may offer some insight when he stated. “Man creates artificial conditions. This is Architecture. Physically and psychically man repeats, transforms, expands his physical and psychical sphere.” It is the speculative capacity to invent and construct alternative models that host environments that may play witness to them. The desire is to see architecture as active, anticipatory and adaptive through continuous exchange that are real-time and behaviour-based. Through this expanded field of understanding we can consider materiality as something that is not inert and finite but that is life-like, evolving and aware. If Hollien believed “All is Architecture” today our studio would argue my mantra that. “All is Behaviour”.

Architecture over the last fifty years has witnessed a revolution within communication, computation and technological progress. Is is important to remind ourselves that architectural experimentation has always been an active participant in exploring, prototyping and speculating our relationships with these technologies. To address these challenges we must construct frameworks that allow us to co- construct solutions. Computation and design research affords us this capacity to create and communicate new solution spaces and understandings. Environmental conditions, machine learning and collective building will challenge the next generation of design to explore computation beyond form and geometry. Architecture will construct new territories for enquire: programmable matter, emotion space, behavioural ecologies may signal just s few. The studio approaches architecture as an infrastructure and technology as culture. Towards a behavioural architecture…

Tutors: Theodoro Spyropoulos Assistant: Mostafa El-Sayed

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INDEX

Thesis Statement Inroduction Mobility Communication Control Systems Learning Self Organisation Packing Self-Organisation Local Packing Self-Structuring Hight Population Self-Structuring

Machine Learning Behaviour Recognition Sonar Mapping Lidar Mapping Materiality Silver Gala Exhibition Materialisation/Mobility Mobility/Machine Learning Prototype System Scenario Epilogue

17 18 46 88 144 122 128 134 150 160 170 188 194 208 214 242 256

4057

THESIS STATEMENT

4057 4057 evolves around the topic of culture, where culture is defined as what most of the people do most of the time.

In the cities we live in, what we do is defined by the surrounding built environment and our routines are determined by the opportunities and obstacles created by the infrastructures of the city. Therefore, 4057 introduces a new system within the urban infrastructure, which interacts with the user routines of the cities, in order to redefine them, to redefine urban culture.

The way the existing infrastructure defines our culture and routines is static and non-dynamic, thus making it impossible for the existing urban systems to adapt to the users needs. In this framework, the emergent behaviours and organisational abilities of 4057 make it possible for the urban environment to respond to the user and the user’s needs, in order to create cities that are intelligent, interactive timebased space generators.

A system, which is able to reflect user patterns and information on the existing infrastructure in order to optimise space, as described before, must be intelligent.

For that reason we investigated the concept of learning to acquire intelligence within our system. However it was important for us that the system can take decisions as well. Through further experimentation we came to the conclusion that space negotiation is the key to creating a dynamic self-organizing system. These aspects defines at large the framework of the research done for the realisation of the 4057 project.

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INTRODUCTION 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. “ The most generic definition of culture is a way of life. In a higher perspective it can be defined as a collection of routines, information and senses from the moment mankind started reflecting on their own experiences. Which throughout history articulates in many forms, from cave paintings to literature and now media. These can be understood as technological developments. However, if technology can change how people express themselves or the way they live, how can it be so distant from culture? Literature, paintings, design can be easily identified as cultural aspects.

Why are technology and culture considered as complete opposites?

The separation of technology and art is invalid. Technology is culture. Every innovative idea, every analytical approach to the environment results to a development.

A ground breaking artwork is as important as responding to a lacking feature of our lifestyle, that will change the quality of life in the world. Technology and culture are in a cycle of constant feeding and improving each other. Being innovative and able to change the perspective of things, makes technology 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 being innovative and responsive. The way technology is related to culture has to be innovative and responsive as also the way it interacts with the environment. This affects the way we live, experience and express ourselves. As it changes the culture, it defines the way we live.

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Alphaville (1965) - Jean-Luc Godard Alphaville [Fig.1] is a movie by Jean-Luc Godard which he intended to call “IBM versus Tarzan” in the first place. It is a movie about a city and its people that are controlled by a computer and sterilized from their emotions by it. While the movie has a technophobic approach, it is important as a cultural product, because there is no separation between technology and culture. While this movie inspired lots of science fiction movies like “Blade Runner” and “Dark City“. and the dystopian environments created in those movies are similar to the one in Alphaville, technology and culture are much more disconnected than in Jean-Luc Godards film.

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 about their environment.

By challenging every aspect in the environment, society is able to create, enhance and articulate its culture. The faster the process, the more understandable the environment becomes and 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, and we live in constant change.

Fig.2

Fig.3 21

INTRODUCTION 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.“ Besides being able to represent or reach over to a certain information, it is also very important to be able to evaluate or interpret it more. Interfaces create a medium to discuss and articulate data. Through that interaction opportunities are created to be innovative and responsive towards the environment. It is equivalent to the aforementioned relation between technology and culture. Because when an interface is anticipated 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 an interface indicates something more architectural or complex, it should be understood as process of designing a medium to interact, that creates possibilities to be involved. The same way technology is culture, interface is the medium to be present and creative with that given culture. Through the interaction with the interface every piece of information can be made visible, or even transformed into something more than it indicates by itself.

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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 23

INTRODUCTION 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 leave traces of our experiences in the city in the first place. For example, to set a pin point to a place we use multiple layers of data which can be either geo-location, raw data or sharing an experience with someone else. But through 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 changes how society experiences 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, the whole image and pattern of the city changes.

Time, population, trends and the physical infrastructures of the city define the active pattern of the city. The more complex that it gets, these infrastructures of the city are blending more into each other. As a whole, it creates a system beyond perception and creates new solutions to respond to 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 to the different demands, which creates a synthesis with each existing infrastructure by cooperating and blending in to our routines.

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Fig.6

Fig.7

SENSE OF PATTERNS

Sense of Patterns is a project by Mahir M. Yavuz, a series of printed data visualizations that marks the behaviours of masses in selected public spaces in Vienna.

The observations resolve 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 intends to unfold tourist trends and interests by data mining publicly shared photos of people who have 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 25

MOBILITY

URBAN MOBILITY & INFRASTRUCTURE From the industrial era, mobility has been a huge part of urban life. People are spending substantial amounts of their times in the streets, walking, cycling, using their personal vehicles or crossing the city with public transportation. One of the crucial things that needs to be mentioned, is that urban mobility is highly dependent on its infrastructure. It is defining the way people perform their routines, according to the congestion or the duration that selected transportation method requires. Therefore the way people become mobile is according to the designated infrastructure.

The only way people can become mobile independently from an infrastructure is by using 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 the transportation infrastructure as a network we can say that the linear tracks that intersect, create an opportunity to expand. Which reflects to the city as a station, junction or a stop. In that sense those intersections or this infrastructure as a whole can be understood as an interface which creates a medium to interact with and influence other systems. For example transportation infrastructure systems have great influence on architecture, as it defines 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 built higher than before to infuse power, but being vertically mobile changed the way we design. However, in the contemporary world the city is defined by users over shared informations and experiences every day. The way we understand and design our built environment is changing as fast as the information infrastructure allows it to.

Fig.9

Fig.10

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A node is an intersection of two modes or two modal lines and is, thus, na potential interchange. Some nodes are materialised in space as railway stations, airports, network hubs and, ultimately, cities. (p.13) Nodes exist at various sizes and locations. Urban nodes typically consume large swathes of Fig.11 Shenzhen Bao’an International Airport, Shenzhen, China land, due to the open expanse of the infrastructure landscape of platforms, good yards and railways lines(or gates, hangars and runways), 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 Fig.12 interactivity.

If the growth of a node accelerates, if its mobile programmes and infrastructure amass and economically accumulate at mega scales, a city agglomerates as a ‘transmetropolis’.

Fig.13

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MOBILITY THE MAP

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 ‘event-space’, 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!

Even though the functionality of mobility is linear the concept of it is volumetric. The intention is to arrive through the shortest path to wherever the coordinates of the destination are. However when it is considered how urban mobility operates, the destination point is always projected on two dimensions, on the ground floor, and when arriving to the projected point another vertical infrastructure connects people to the 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 operate in two dimensions.

Urban mobility can not be considered as simply moving from point A to B, since it includes 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 change the meaning of being present, it is important that urban mobility is responsive to the demand of being present in multiple locations much faster.

If we consider the way and the speed with which the information is acquired today, not even urban mobility is sufficient enough for that pace. Moreover the infrastructure of urban mobility is not evolving according to the urban pace or population growth. Therefore even though a city as we understand it today is designed to function perfectly, it is not going to be efficient in the next 10 years. Therefore the design of this infrastructure should be adaptive, responsive to the social and physical needs and to allow urban mobility to function accordingly to the daily routines and demands. The mobility should be about the journey and the destination itself as a whole and not a collection of arrival points designated by the infrastructure.

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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 used in an ambiguous way. Transportation infrastructures, vehicles or stations are all spaces that people are present in in a daily basis, although they do not exactly indicate a place. The infrastructure as a space is used as a passage, which, in some cases, does 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 this identity. However the infrastructure located 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 an ambiguity because, while it is reflecting the urban culture on ground plane, by its use or by the role infrastructure projects on people, it is a non-place.

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

Fig.15 29

MOBILITY THE MAP

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) The Urban pattern is one of the most unique qualities of a city. While creating a unique identity for each city through its infrastructure, it is also a reflection of the urban culture. The oldest way of representing these overlapping patterns is mapping the city. Although it is being understood as a manner of pathfinding, with the contemporary representation tools, maps can be created in more advanced of ways.

The core of the idea of mapping is still quite the same, but it is being used to indicate more 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 create an expression of it more than just reflecting its physical qualities. In that sense it is obvious that the city maintains multiple layers of data which are 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 a 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.

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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.

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COMMUNICATION 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 maintain a 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 system, there is a level of signal/boundary communication, which creates an attraction or impulse for the system to organize itself. While the signal creates this impulse, the boundary refines the level of interaction and determines which impulse is related to the system and which is irrelevant.

It is important to evaluate this level of communication in two stages. The 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 uderstanding 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 learner’s ability to produce meaningful utterance sequences.

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

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Fig.18

Scanning – 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. – From Biographical Sketch: W Grey Walter by Walter J Freeman, Encyclopedia of Cognitive Science (2003) Machine-Machine Communication: Signal-Boundary based communication.

The 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

Machine

Human Human

Human Machine

Fig.19

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Fig.20

COMMUNICATION 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 mentioned before, learning is a huge part of language development, which makes it a high level of communication. At the same time, the level of learning is highly dependent on senses. The more input the sensory-motor receptors receive, the 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, since 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, but to receive more iput or to become more social.

The medium of communication therefore is changing and is evolving to a different interface, detached 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 a body can perform depends at some extent on communication.

Inputs received from the eyes are signals to the brain, so places are recognized, therefore simple behaviours, such as way-finding or obstacle-avoiding, are reliant on communication. This process, simple behaviour generation, is identified by Grey Walter as scanning and can be seen in many forms in animals, like echolocations or pherormone tracking in social insects.

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

Recently Facebook held an experiment for artificially intelligent bots to perform simple trading tasks. In this process the two bots manipulated the English language and came up with shortcuts to make the process easier for themselves. Eventually, the language they used became completely incomprehensible and unique to them and Facebook shut down the experiment.

Fig.21

When the language they used got investigated, the researchers found some rule sets they were using in their own language. In a weird way they were able to conclude the tasks they were given, however the language was completely unknown to the developers.

After some investigation it was understood that they were relating how many times they use the word with the amount of the desired objects.

Fig.22

Fig.23 35

CONTROL SYSTEMS The design goal is nearly always underspecified and “the controller� 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. In some cases, these systems function without even a human intervention.

Open-loop Control System

This type of control system functions over a given direction. There is no automation, feedback loops or output evaluation in the control system, therefore output quality is dependent on the 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.

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Electronic System

Open-loop Control System

Closed-loop Control System

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CONTROL SYSTEMS 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 to have a closed-loop control system. In such a system the 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, the boundary refines itself, every time an output has been created through its feed-back loop. When the user demands (which can be thought as an input) and the output performance are 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 the system will work.

In that case, the type of input and the possible outcome are limited, and the system’s adaptation to different conditions is low. For example a rotating solar panel can increase its efficiency, but it does not know the required energy for the system it is 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 to work efficiently or not, as 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 the interconnected systems. This communication can be for example transportation infrastructure to a vehicle or a user to a vehicle, and 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.

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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).

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LEARNING

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 on the subject of creating artificial intelligence, it is commonly accepted that sensory-motor applications and communication have a great importance for the emerging 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 of those, it is fair to say that the agent is able to learn. Therefore, to be intelligent or to emerge new behaviours, an agent needs to learn. Learning even the simplest declared behaviours can lead to complex behaviors which allow the 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 though it is not proven, is highly related with evolution. For something to evolve, for instance for a species to evolve or adapt, the actions it takes under the same cirqumnstances 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 the chance of survival. This basic Darwinist approach to evolution indicates that learning is important to generate species that can adapt and take better choices.

For a system, which is able to adapt and respond to a variety of situations, it is important to learn through its own experiences or by evaluating the input from its environment. That input can be coming from communication with the systems own agents, the 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 better response for every situation.

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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 also an analysis of their complex behaviour.

CORA circuit: The second generation of tortoises have the ability to learn and develop reflexes. In that sense, this application was the mechanical equivalent to 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 41

SELF-ORGANISATION 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-organisation is the essential way the biological and non-biological worlds organise themselves. This concept has proven itself useful in biology, from the molecular to the ecosystem level, in bio-chemical reactions and in the physics of non-equilibrium processes. Cited examples of self-organising 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-organisation 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 organising system. He created a parallel processing computation system by changing the states of the system, therefore the calculations had to be made simultaneously.

In a self-organisational system, multiple agents can form local packs. One of the crucial features of a self-organisational system is that it does not need a leader or centralized controller. The system is able to adjust and respond through local interactions. However when the local packs might perform different behaviours, the coherency is maintained.

Self-organisation is an agent-based system which contains decentralized intelligence that operates on local rules. Although a single agent is not capable of performing a task, collectively the system can achieve complex behaviours. Through communication the system can overcome complex problems such as finding food or building ant bridges. [Fig.28]

Fig.27 42

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.

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SELF-ORGANISATION

Fig.29

Self-organising 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-organisation has been used as a paradigm to design adaptive and robust artificial systems (Gershenson, 2007). 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-organising elements.

Fig.30 44

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.

Self-organisation is one of the driving forces of the proposed system, since architecture is seen as a system that can respond to the city scale. While the material and structural behaviours of the single agents are designed to perform simple tasks, a self-organised group of agents is capable of achieving higher goals. Their performative qualities as a pack, are designed to structure and organize on themselves the urban scale. Through their ability to understand the environment requirements and evaluate the inputs from the user demands, they are able to organise 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.

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

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SELF-ORGANISATION

Self-organisation: The system organises itself, but there is no “self�, no agent inside the system doing the organising.

Fig.31

Fig.32

Metamorphic robotic systems:

This system consists of a group of independently controlled mechatronic modules, each of which has the ability to connect with, disconnect from, and climb over adjacent modules. All modules have the same physical structure (homogenity), and each module is autonomous from the viewpoint of computation and communication.

The modules are introduced in two different types, hexagonal and square. However compared to the square modules, the hexagonal ones have more joint reconfigurations, therefore it provides more flexibility and a variety of assembly possibilities to the system.

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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 always have the same experiences. Therefore it might create different behaviour patterns among the system. Which are beneficial for the system to serve accordingly to the local demands and benefits. As the differentiated agents interact with each other while performing a variety of tasks, the way they structure and organise themselves will change. Such a diversity is also important for optimising and increasing the efficiency of the system so it can understand different existing patterns and provide a variety of new patterns that fit the demands and requirements of their environment.

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 a diversity and level of organisation in the system can be achieved by designing agents that are able to communicate with each other and the user, recognize their environment and users and additionally are able to learn through their experiences and shared information. When such an interconnectivity is achieved among the agents, they can perform an infinite amount of tasks inside the city, while they are learning local patterns and requirements. Simple local evaluations and adaptations can help the whole urban system to achieve a responsive, evolving urban infrastructure.

When not being dependent on a designated infrastructure, a higher mobility can be achieved. With such an adaptive infrastructure, the mobility is going to have the potential to change according to informatics, represent the hidden layers of the city and create a reflection of the urban culture. The city is not going to be planned two dimensionally anymore. Users are going to claim their right to decide on urban design and contribute through their mobility.

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PACKING

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Packing as a system, refers more to optimisation rather than to self-organisation. However, when agents, which are packing, develop behaviour, the system reorganizes itself as a result of negotiation for space. With this logic we started to experiment on how different types of negotiation may result to different packing systems.

By adding simple behaviours to a sphere packing system, we experimented on how space can be seen as a medium of negotiation.

In this specific model, space is represented as a cube and the agents have to expand to the limits of the space. However, when a specific sphere requires more space, it needs to negotiate for space and influence the spheres expansion rate to achieve a stabilized condition.

When behavioral complexity is added to the system, negotiation for space and occupation of space are drastically changing. In this case, two different types of agents, that have different behavioural qualities, are fighting for space.

While the agents of the first set are trying to stay together, the agents of the second become more individual in space. Therefore, the system constantly changes its state until it reaches stability, which also results in domination of the one set on the other in space.

However, the stabilisation in the system always results in a fully packed environment, which initiates the conversation about the environment and how some input from the environment can affect the process of stabilization by increasing the likelihood of the system to reorganize according to this input.

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PACKING SPHERE PACKING SYSTEM SAME RULE BASED AGENT

When the same rules, expansion rates and random position assigning are applied, the possible sets of behaviours that could be achieved are not versatile enough. Although every time the game is initiated, it is possible to achieve an infinite amount of results, these results are predictable and there is no optimisation of space or communication among the agents. These rulesets are restrictive to achieving any kind of behavioural qualities from the agents.

INITIALISED AGENT POSITION TOUCHED THE BORDER AND STOPPED EXPANDING TOUCHED THE NEIGHBOUR AND STOPPED EXPANDING

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51

PACKING SPHERE PACKING SYSTEM SAME RULE BASED AGENT

Due to the former findings, the rulesets for this experiment are slightly changed. In this application agents are adjusting their expansion rate according to their positions. Depending on their location, agents are growing slower or faster. The closest an agent to the border of the cube is, the smallest its expansion rate is, and the closest to the center of the cube it is, the biggest its expansion rate is. While this ruleset gives more optimised results, still the behavioural qualities achieved with this application are not enough. Therefore, more adjustments are required to be made.

INITIALISED AGENT POSITION TOUCHED THE BORDER AND STOPPED EXPANDING TOUCHED THE NEIGHBOUR AND STOPPED EXPANDING

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PACKING SPHERE PACKING SYSTEM SAME RULE BASED AGENT Although in this application the time factor is added to the rulesets, the results are the same with the first application. The only difference that can be observed compared to the first experiment is that the results are acquired much faster. While this application might achieve different results, if the agents appear in their positions in different times, it still lacks some behavioural qualities. As a result, the experiments show that the optimisation depends also on the position and expansion rate.

INITIALISED AGENT POSITION TOUCHED THE BORDER AND STOPPED EXPANDING TOUCHED THE NEIGHBOUR AND STOPPED EXPANDING

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PACKING SPHERE PACKING SYSTEM EXPANSION UNTIL STABILISED After proving that space optimisation depends both on the expansion rate and the position adjustment of the agents, the expansion rate according to the position is added as a factor to this ruleset. In this application the results are much more promising in the context of optimisation and agents are performing closer to cooperative behaviour. While the acquired results are closer to the goal, the result is still not perfect, due to the lack of learning skills and an application of machine learning.

INITIALISED AGENT POSITION TOUCHED THE BORDER AND STOPPED EXPANDING TOUCHED THE NEIGHBOUR AND STOPPED EXPANDING

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PACKING SPHERE PACKING SYSTEM POSITION BASED AGENT UNTIL STABILISED After proving that space optimisation depends both on the expansion rate and the position adjustment of the agents, the expansion rate according to the position is added as a factor to this ruleset. In this application the results are much more promising in the context of optimisation and agents are performing closer to cooperative behaviour. While the acquired results are closer to the goal, the result is still not perfect, due to the lack of learning skills and an application of machine learning.

INITIALISED AGENT POSITION TOUCHED THE BORDER AND STOPPED EXPANDING TOUCHED THE NEIGHBOUR AND STOPPED EXPANDING

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PACKING SPHERE PACKING SYSTEM ATTRACTOR BASED AGENT UNTIL STABILISED To see if the introduction of an attractor in the system affects the behavioural qualities of the agents, one border of the cube is assigned as an attractor within the environment. However, when all of the agents are attracted to the same location in space, their expansion rates become almost the same, and the results aquired from this application are the same with the ones from the position adjustment experiment. The final volumes of the agents have minor differences and the goal of space optimization is not achieved. Therefore, the previous application is the best ruleset for space optimization from all of these experiments.

INITIALISED AGENT POSITION TOUCHED THE BORDER AND STOPPED EXPANDING TOUCHED THE NEIGHBOUR AND STOPPED EXPANDING

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61

PACKING

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RADIUS 1:1

RELATIONSHIP WITH THE BOUNDARY

For this application, while the expansion rates remain the same for both sets, the pace of movement of the black agents is three times faster than the pace of the white agents. While in the beginning black agents seem dominant in space, black and white agents reached a static condition in total equality, because both sets had the same maximum volume.

RADIUS AROUND 1/3 DEPENDING TIME

RELATIONSHIP WITH THE BOUNDARY

In this application, the speed of white agents is maximized, and the speed of black agents is minimized. However, the expansion rate of black agents is kept at maximum level while the expansion rate of white agents is minimised. As a result, black agents dominate the environment, because they have the capacity to expand more in the same amount of time.

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PACKING

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RADIUS AROUND 1/3 DEPENDING TIME

RELATIONSHIP WITH THE BOUNDARY

When the same conditions are reversed between sets, the dominating set changes. If the attraction level changes, the dominating set depends on the factor of expansion rate. It is important to note that the different sets have different attraction levels among their agents.

Therefore, the attraction level only affects the position and the movement of the agents.

RADIUS AROUND 1/3 DEPENDING TIME

RELATIONSHIP WITH THE BOUNDARY

In this application, the pace and expansion rate of black agents are maximized and of white agents are minimized. The results are the same as when only the expansion rate, and not the pace, was maximized for the black agents and minimized for the white agents. This means that the pace only determines how fast the system is going to reach a static stabilisation, when the expansion rates are already determined for the sets of agents.

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PACKING

In this setup only one agent has behavioural qualities and goals. The black agents expand with the same rate and stop when they touch anything and the orange agent is competitive and tries to occupy the most space possible. To achieve that goal, it pushes the other agents and creates more space for itself, thus reaching the maximum volume until there is no space left.

AGENT

FROZEN NON-COMPETETIVE AGENT

EXPANDING NON-COMPETETIVE AGENT

SPACE THAT AGENT OCCUPIES

AGENT MAKING SPACE FOR ITSELF

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AGENT SIZE

In this case, all agents have the same ruleset and the difference is that there are two competitive agents. The two orange agents are pushing each other and the black agents, and they are trying to occupy the most space they can. As a result, every time the game is played, it ends up with the same volumes. Since both orange agents have the same qualities, they cannot dominate each other and occupy the same amount of space.

AGENT

AGENT

EXPANDING NON-COMPETETIVE AGENT

FROZEN NON-COMPETETIVE AGENT

SPACE THAT AGENT OCCUPIES

AGENT MAKING SPACE FOR ITSELF

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AGENT SIZE

PACKING

STARTING POSITIONS

STARTING POSITIONS

CRASHING MOMENT POPULATION

DAMAGE CAUSED BY CRASH

POP RE

CRASHING MOMENT INDIVIDUALITY

DAMAGE CAUSED BY CRASH

IND SU

After individually all of the behavior applications have been tested, the agents are assigned with more complicated sets of qualities. Expansion rate is set as a variable that depends on the situation. More interaction between the agents of a set results in higher expansion rate and more interaction with the agents of the other set results with negative expansion rate for the agents. Pace and level of attraction are used differentiating factors.

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POPULATION RECOVERY

POPULATION INVADE

INDIVIDUAL SURVIVAL

INDIVIDUAL EXTINCTION

FINAL STATE

FINAL STATE

For the black set of agents, attraction within the family is the highest and for the set of white agents pace is the highest. In the beginning, white agents dominate, because they are interacting in a higher pace, however as time passes and black agents cumulate, they dominate the environment and the less white agents interact with each other, the more they shrink. Eventually the system results in a diversity of results in one play.

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PACKING SAME KINDS ATTACHES TO 4 OTHER ... EXPAND

REPULSION TO OTHER FAMILY

If ...

ONE KIND COLLIDES WITH TWO OTHER KIND ... SHRINK

ATTRACTION WITHIN THE FAMILY

SET 001 Attraction within Set Repulsion to the other Set Positive Expansion Factor within Set Negative Expansion to the other Set SET 002 Attraction within Set Repulsion to the other Set Positive Expansion Factor within Set Negative Expansion to the other Set

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For this application attraction and repulsion levels are set the same to experiment on how the agents are going to respond to an attraction. As a result, they all located around the attraction area and the agents that are close to the other sets region shrink to a minimum volume.

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PACKING

INDIVIDUALITY

STARTING PLACEMENT OF AGENTS

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WHITE GROUP CONTIONOUS GROUP

EARLY GROUPS

BLACK GROUP CONTIONOUS GROUP

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PACKING SAME KINDS ATTACHES TO 4 OTHER ... EXPAND

REPULSION TO OTHER FAMILY

If ...

ONE KIND COLLIDES WITH TWO OTHER KIND ... SHRINK

ATTRACTION WITHIN THE FAMILY

SET 001 More Attraction within Set Repulsion to the other Set Positive Expansion Factor within Set Negative Expansion to the other Set SET 002 Attraction within Set Repulsion to the other Set Positive Expansion Factor within Set Negative Expansion to the other Set 74

For this application the attraction within the white set is increased while in the black set it is reduced. As a result, white agents surround the black agents and dominate in space. Most of the black agents disappear and a small amount of them regionalise in an area and survive as a small cumulation.

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PACKING

INDIVIDUALITY

STARTING PLACEMENT OF AGENTS

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EA

ITY

WHITE GROUP DOMINANT GROUP

EARLY GROUPS

BLACK GROUP RECESSIVE GROUP

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PACKING SAME KINDS ATTACHES TO 4 OTHER ... EXPAND

REPULSION TO OTHER FAMILY

If ...

ONE KIND COLLIDES WITH TWO OTHER KIND ... SHRINK

ATTRACTION WITHIN THE FAMILY

SET 001 Attraction within Set Repulsion to the other Set Positive Expansion Factor within Set Negative Expansion to the other Set SET 002 Attraction within Set Repulsion to the other Set Positive Expansion Factor within Set Negative Expansion to the other Set 78

When the conditions are reversed to having the black set with a higher pace, the result is that the black set completely dominates the attraction area. While the white set survives as small groups further away from the attraction area, most of the agents are destroyed.

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PACKING

INDIVIDUALITY

STARTING PLACEMENT OF AGENTS

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WHITE GROUP GREATER IN NUMBER CONTINOUS GROUP

EARLY GROUPS

BLACK GROUP LESS IN NUMBER CONTINOUS GROUP

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PACKING

SET 001 Attraction within Set Repulsion to the other Set Positive Expansion Factor within Set Negative Expansion to the other Set SET 002 Attraction within Set More Repulsion to the other Set Positive Expansion Factor within Set Negative Expansion to the other Set 82

While adjusting attraction levels changes drastically the dominating set, it is also important to see what difference can adjusted repulsion levels cause. For this case the black set has the highest repulsion levels towards the white set, therefore black agents stay in a single region. The white agents, which is affected from the higher repulsion, are distributed in a bigger and a smaller region in the attraction area.

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PACKING

INDIVIDUALITY

STARTING PLACEMENT OF AGENTS

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WHITE GROUP SMALLER IN SCALE GROUP CONTIONOUS

EARLY GROUPS

BLACK GROUP BIGGER IN SCALE GROUP CONTIONOUS

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PACKING SAME KINDS ATTACHES TO 4 OTHER ... EXPAND

REPULSION TO OTHER FAMILY

If ...

ONE KIND COLLIDES WITH TWO OTHER KIND ... SHRINK

ATTRACTION WITHIN THE FAMILY

SET 001 Attraction within Set Repulsion to the other Set Positive Expansion Factor within Set Negative Expansion to the other Set SET 002 Attraction within Set Repulsion to the other Set Positive Expansion Factor within Set Negative Expansion to the other Set 86

When the repulsion levels are maximized for both sets, no bigger regionalisations or dominations are observed. The sets are distributed to different locations and cumulate in different regions. The important knowledge acquired from these experiments is that, while attraction determines the dominating set, the level of repulsion determines the regionalisations within the system.

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PACKING

INDIVIDUALITY

STARTING PLACEMENT OF AGENTS

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WHITE GROUP MORE DENSE GROUP CONTIONOUS

EARLY GROUPS

BLACK GROUP LESS DENSE GROUP SPARSE

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SELF - ORGANISATION

Reaction Diffusion

SELF - ORGANISATION 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 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, 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 their diffusion. Therefore we will use it as a tool to generate self-organizing units in the urban 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 uneven concentrations spread out across the grid, and A diffuses faster than B.

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Chemical B is removed at a given “Kill” rate

The system is approached by using two numbers for each grid cell for the local concentrations of A and B.

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.

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By checking all neighbours, the diffusion rate is recalculated with different amounts, corresponding to the reaction rate.

The system is able to create such patterns with simple and local rules.

SELF - ORGANISATION

Seed

Rules Diffusion U Diffusion V Feed Kill

Same rates with different seeds

1.0 0.3 0.054 0.062

Seed

Rules Diffusion U Diffusion V Feed Kill

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1.0 0.3 0.054 0.062

Seed

Rules Diffusion U Diffusion V Feed Kill

1.0 0.3 0.054 0.062

Same rates with different seeds

Seed

Rules Diffusion U Diffusion V Feed Kill

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1.0 0.3 0.054 0.062

SELF - ORGANISATION

Seed

Rules Diffusion U Diffusion V Feed Kill

1.0 0.3 0.054 0.062

Same seed with different rates Seed

Rules Diffusion U Diffusion V Feed Kill

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1.0 0.3 0.04

Seed

Rules Diffusion U Diffusion V Feed Kill

1.0 0.3 0.054

Same seed with different rates Seed

Rules Diffusion U Diffusion V Feed Kill

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1.0 0.1-0.3-0.5 0.054 0.062

SELF - ORGANISATION

Seed

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

Same seed with different rates

Seed

Rules Diffusion U Diffusion V Feed Kill

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- 0.9 0.2 - 0.7 0.054 0.062

1.0

Type 1 Higher Population of Black Small Population of White

Different Conditions 1. Diffusion (Transaction) Speed of Populations 2. Feed and Kill Rates 3. Additional Forces Gravity/Rotation 4. Regionalisation 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 units. Reaction diffusion systems are self-organizing systems which reach stabilisation dependinging on environmental conditions. Whenever the conditions change, the system has to reorganise and the agents have to negotiate for space according to the state changes of the environment.

In our experiments we use a reaction diffusion setup, which simulates some changes in the environmental conditions. Whenever the system stabilises, we change some conditions in our computational environment to simulate the reorganization.

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SELF - ORGANISATION

100

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.

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SELF - ORGANISATION

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SELF - ORGANISATION

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When we decided that we need input from the environment we started to investigate reaction diffusion systems. Reaction diffusion systems are self-organizing systems that reach stabilization depending on environmental conditions. Whenever these conditions change, the systems have to reorganise and agents have to negotiate for space according to the changed states of the environment.

In our experiments we use a reaction diffusion setup which simulates some kind of change in environmental conditions. Therefore, whenever a system stabilises, we change some conditions in our computational environment to simulate the reorganization.

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SELF-ORGANISATION SELF - ORGANISATION

Organizational Model

Ferrofluid Experimet FERROFLUID

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SELF - ORGANISATION

The existing infrastructure has different user concentrations in different moments of the day.

In these different moments, also the different activities, as shown in the example of Oxford Circus, influence the concentration of users.

However while the utilization of space is changing, the space is not able to respond to the different user needs and activities. The repetitive patterns created by the users are not able to reflect on the infrastructure, so the infrastructure defines the routines in a non-dynamic manner.

That is why the information from the user habits and routines must be fed back to the infrastructure, so it can process this information, in order to re-organize itself based on the user’s routines and needs.

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SELF - ORGANISATION

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SELF - ORGANISATION PATTERN GENERATION

Data Map

DENSE - SPARSE 112

Height Determination Topological Deformation

Populate

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SELF - ORGANISATION PATTERN GENERATION

Data Map

DENSE - SPARSE 114

Height Determination Topological Deformation

Populate

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LOCAL PACKING To reach a target with higher population, we specify 3 types of behaviour. Local packs have the ability to identify a target and send signals to the individuals. If a local pack is within the range of an individual, the individual joins the pack to reach the target.

Individual behavior

Random Walk

Cluster Search

Target Seeking

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SELF-ORGANISATION LOCAL PACKING

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LOCAL PACKING

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LOCAL PACKING

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LOCAL PACKING

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SELF-STRUCTURING Organisational Packing To operate in a real world situation, when the units receive a signal, they need to organise themselves from a distributed state to the state of being fully packed. Units within the radius of the signal will search for the closest agents to form clusters. These clusters move towards the target to become fully packed.

1 - Distributed

2 - Target Signal

3 - Distance Check

4 - Local Packing

5 - Full Packing

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Reaching Target After the units form local packs on a two dimensional level, to reach a target in a third dimension, we use centroids and gradients. A centroid represents the most dense part of the pack. According to their distance from the centroid, agents are labeled with different colours that represent their behaviour to reach a target. up on the z axis.

Pack Target

Centroid

Target

Gradient 125

SELF-STRUCTURING

Local Packing

Distributed

Full Packing

Centroid

Gradient

Gradient

Labeling

Target Reached

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Local Packing

Distributed

Full Packing

Centroid

Gradient

Labeling 127

Gradient

Target Reached

SELF-STRUCTURING

Distributed

Local Packing

Full Packing

Centroid

Gradient

Gradient

Labeling

Target Reached

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HIGH POPULATION - SELF-STRUCTURING

Enclosed Space C

Enclosed Space A

Enclosed Space B

Enclosed Space Structuring Units Activated Units 130

Enclosed Space A

Enclosed Space A

Enclosed Space A

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HIGH POPULATION - SELF-STRUCTURING Enclosed Space B

Enclosed Space B

Enclosed Space B

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Enclosed Space C

Enclosed Space C

Enclosed Space C

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HIGH POPULATION - SELF-STRUCTURING STATE 1

STATE 2 TIME BASED PACKING RELATIVE SPEED ACCORDING TO INDICATOR NEEDED HEIGHT

INITIAL STATE

PATTERN FORMATION

PATTERN GENERATION BY REACTION DIFFUSION RULESETS

STATE 1: SIGNALING TO PACK

MIN AMOUNT OF U CREATE A STRUCT

STATE 2: ARRIVING TO FULL PACK STATE

SELF STRUCTURING THROUGH SUBSTRACTION

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STEP 1: CREATING THE NEI CONDITIONS

STATE 3

PATTERN 3

GENERATION

MOBILE PACK

STATE 1: SIGNALING TO PACK

MIN AMOUNT OF UNIT THAT CREATE A STRUCTURE

STATE 2: ARRIVING TO FULL PACK STATE

SELF STRUCTURING THROUGH SUBSTRACTION

STEP 1: CREATING THE NEIGHBOURH CONDITIONS

STATE 3: BEING AWARE OF YOUR NEIGHBOURS POSITION

SUPPORTING THE STRUCTUR

SELF STRUCTURING THROUGH ADDITION

STEP 3: AFTER THE POSITIONS HAS B TAKEN INBETWEEN TWO UN THE THIRD ONE PUSHES IT O TOP

STATE 3: COMMUNICATION AND STATE CHANGE

UNIT DESIGN 135

UNIT STARTS TO ACTIVATE INCREASING THE TENSION BETWEEN CONNECTED

MACHINE LEARNING

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Since we define environmental input as a key feature to our systems dynamism, it is important for us to define a recognition system which will allow our agents to have their own cognition.

To decide the recognition system, we experiment with several concepts including language based systems. However, controlling the agents with a specific vocabulary is over controlling and creates unbiased situations for the system, because it is dependent from the individual controls. We want our system to be able to understand and analyse so it can express a behaviour. In order to achieve that we enhance the system with sonar recognition and signal based communication.

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MACHINE LEARNING

There are 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 Examples are introduced as input to the computer, and the desired outputs (labeled data) are given by a “teacher�. The goal is for the computer to learn a general rule that maps inputs to outputs. In specific cases, the input signals can be only partially available, or restricted to a specific 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, so it finds structures in the input on its own. Unsupervised learning can be a self-standing goal (discovering hidden patterns in data) or a means towards an end (feature learning).

In this case, no teacher exists, while the computer might be able to teach the user new things after learning patterns in data. These algorithms are particularly useful in cases where the user does not know what to look for in the data.

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SEMI-SUPERVISED LEARNING The computer is given only a partial training signal: a training set, with some of the target outputs missing.

In the absence of labels in the majority of the observations, but present in few of them, semi-supervised algorithms are the best for model building.

REINFORCEMENT LEARNING Training data (in the form of rewards and punishments) are given only as feedback to the programme’s actions in a dynamic environment, such as driving a vehicle or playing a game against an opponent.

This method aims at using observations gathered from the interaction with the environment to take actions that maximize the reward or minimize the risk. A reinforcement learning algorithm (called agent) continuously learns from the environment in an iterative way. In this process, the agent learns from its experience with the environment until it explores the full range of possible states in this environment.

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MACHINE LEARNING 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 an environment, where intelligent agents can be trained by a machine learning algorithm, we can conduct the experiments of training intelligent and self-adaptive architectural systems.

(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 – Action 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.

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The ML-Agents SDK enable the use of Reinforcement Learning to conduct machine learning experiments. As mentioned above, the reinforcement learning uses the relationship between the agents and the environment to acquire classified data or a learned behaviour pattern of agents within a task.

This method aims at using observations gathered from the interaction with the environment to take actions that maximize the reward or minimize the risk. A reinforcement learning algorithm (called agent) continuously learns from the environment in an iterative fashion. In this process, the agent learns from its experiences with the environment, until it explores the full range of possible states within this environment.

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 observes the environment - The agent takes action -The states of the environment and the agent change -the agents rewards are increased or reduced based on rule set -Optimising behaviours help acquire more rewards 141

MACHINE LEARNING Single Agent Learning Agent:

Single sphere

Goal:

To occupy as much space within the cube

State Space:

Action Space:

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

Environment:

Cube

Observation: Reward Criteria: Volume increase:

Position within the cube Out of the cube: reducing reward increasing reward

Volume -

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)

After 10min Learning

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+

Multi Agents Learning In reinforcement learning for multiple agents in the ML-Agents environment, the relationship between agents is mainly decided by their brains and reward sets.

The brains of the agents decide the learning of actions and how agents observe the environment, which means they learn bahavioural patterns, while the rewards configuration set by the developer determines the actual goal of each agent within the specific task. In this case, brains and reward sets are shared or separated among agents and that is the way the relationship between agents (cooperative and competitive), and their goals changes.

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 behaviour pattern

Achieving cooperating goal with same behaviour pattern

Seperated Brains

Achieving cooperating goal with same behaviour pattern

Achieving cooperating goal with same behaviour pattern

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MACHINE LEARNING Competitive Agents Learning Goal: Reward Criteria:

Agent: Brain: State Space: Action Space: Environment: Observation:

Each sphere has to become larger than the other Sphere A volume is larger Sphere A reward increased Sphere B volume is larger Sphere B reward increased Two Spheres Shared Brain 4 (X, Y, Z, Radius) 8 (X+, X-, Y+, Y-, Z+, Z-, R+, R-) Cube Position within the cube and relative to the other sphere

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

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Volume -

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)

+

Cooperative Agents Learning Goal:

Higher total volume within the cube

Reward Criteria:

Total volume increase: Sphere A & Sphere B reward increased

Agent:

Two Spheres

Brain:

Shared Brain

State Space: Action Space: Environment: Observation:

Volume -

4 (X, Y, Z, Radius) 8 (X+, X-, Y+, Y-, Z+, Z-, R+, R-) Cube Position within the cube and to other spheres

+

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 realtionship between agents, both of them have same rewards with larger total volume.

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MACHINE LEARNING Agent Behaviour Learning Project: OwO, Theodore Spyropoulos Studio, 2015

Twisting

Combination of units and movement

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Crawling

As Machine Learning can process and find rules in complex and indistinct data, it can also be used to explore the agentsâ&#x20AC;&#x2122; complex behaviour.

The behaviours of an agent can be divided into two types, low level and high level. The low level behaviours represent those ones that are local, simple and refer to a single unit. Through the assembly of multi units with different configurations and the combination of their low level behaviours, high level behaviour can be acheived.

However, the combination of behaviours and their performances are indistinct, so machine learning can be used to explore the mechanism of the combination of behaviours to achieve efficient high level behaviours.

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MACHINE LEARNING 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)

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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.

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MACHINE LEARNING

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)

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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.

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BEHAVIOUR

BEHAVIOUR CONNECTIVITY

After defining and investigating the behaviours of a single agent, we increase the number of units to create local packing, which has different behaviours in the system. Through the simple behaviours of a single unit we construct a higher level of organization within a local pack. Depending solely on the interaction, communication and collaboration of multiple units, a local pack is able to achieve higher organisational skills.

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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 Joint types

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BEHAVIOUR CONNECTIVITY

Fixed Joint Connectivity 156

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.

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BEHAVIOUR CONNECTIVITY

Springs and expandable components are used as another example of expansion and contraction.

To investigate more on the deformation, the model is tested under pressuring and stretching forces. Because of the way the modelâ&#x20AC;&#x2122;s structure, this proposed design is not able to keep its deformation.

Fixed Joint Connectivity 158

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BEHAVIOUR CONNECTIVITY

Electromagnet Array 160

Electromagnetic arrays are used in further experiments with magnets. By activating the magnets selectively, the systemâ&#x20AC;&#x2122;s state changes. To make a sphere change slots in the array, the activation and deactivation of the magnets is not enough to achieve mobility. That kind of control requires reverse currency to push from one slot and pull to the other slot.

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RECOGNITION SONAR MAPPING

Since the environmental input is a key feature to our systemâ&#x20AC;&#x2122;s dynamism, it is important to define a recognition system which will allow units to have their own cognition. To decide for a recognition system, we experiment with a couple of control systems like sound or language recognition systems. However controlling the agents with specific dictionary is over controlling and can create unbiased situations in the system. The goal of the system is to be able to improvise through recognised patterns in the environment. To achieve that, it is not beneficial to use command based control systems. The system, through its recognition system, shall be able to understand and analyze in order to perform a behavior. Therefore it is beneficial to enhance the system with sonar recognition and signal based communication.

Sonar Mapping 162

Sonar Mapping These exeriments were conducted after the aspect of mobility proved the point that the performance of the system is highly dependent from the recognition of the existing patterns in the environment. In order to achieve such level of recognition, a sonar mapping system is introduced. Although it is not mapping the environment incompletely, it allows the units to identify objects and aprehend the distance from them. The system is tested with different materials, in order to understand how it responds to materiality. The result is that, although the system recognises an obstacle, it maps each material with different densities of point clouds. That means that the system can also recognise materials.

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RECOGNITION SONAR MAPPING

Material : Foam Distance : 30 cm X Axis Dimension : 50 cm Y Axis Dimension : 70 cm Z Axis Dimension : 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

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

: 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

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RECOGNITION SONAR MAPPING

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

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Sonar Mapping with Sonar Sensor, Processing and Arduino Uno 167

RECOGNITION SONAR MAPPING

168

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RECOGNITION LIDAR MAPPING

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171

MATERIALITY

Expandable Structure - Hoberman Sphere 172

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MATERIALITY A structure with the possibility to expand and shrink can be a suitable solution for prototyping.

A Hoberman sphere is an isokinetic structure patented by Chuck Hoberman, that looks like a geodesic dome, but is capable of folding down to a fraction of its normal size by the scissor-like structure of its joints.

The mechanism of the system can be utilised in the implementation of intelligent agent behaviours.

Structure Configuration

Six basic points 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.

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Surface Tessellation

Surface Vectors

Expandable Structure

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MATERIALITY Scissor numbers & sphere contraction effect Scissor number: 12

Scissor number: 16

Contraction rate: 1.00

Contraction rate: 1.00

Contraction rate: 0.81

Contraction rate: 0.74

Contraction rate: 0.50

Contraction rate: 0.38

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Scissor number: 20

Scissor number: 32

Contraction rate: 1.00

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) 177

MATERIALITY Since the outlines of the systemâ&#x20AC;&#x2122;s behaviour are defined, it is also important to experiment on how such a level of complexity can be materialised. We experiment with different methods and materials to achieve flexibility, since expansion and contraction are of crucial value to our concept. In this experiment we pattern geometries in order to achieve deformation and also modify the application of expansion and contraction from uniformal to performative.

PATTERN PRODUCTION - NEGATIVE MOLDING - FOLDING

CONSTRUCTING DEFORMABLE GEOMETRY

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CONSTRUCTING TOPOLOGY

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MATERIALITY

ORIGAMI FOLDING

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MATERIALITY

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MATERIALITY

PNEUMATIC MODELS Pattern 1

PATTERN UNDER NO PRESSURE

PATTERN UNDER HIGH PRESSURE

Pattern 2

PATTERN UNDER NO PRESSURE

PATTERN UNDER HIGH PRESSURE

Pattern 3

PATTERN UNDER NO PRESSURE

PATTERN UNDER HIGH PRESSURE 183

MATERIALITY

PERFORMATIVE SKIN

ACTUATION

PANEL

BALOONS

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LATEX

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MATERIALITY

POLYURETHANE

LATEX

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POLYURETHANE

LATEX

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MATERIALITY

LATEX

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LATEX

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SILVER GALA EXHIBITION

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SILVER GALA EXHIBITION

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SILVER GALA EXHIBITION

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MATERIALISATION - MOBILITY

The next step is to achieve a unit that can create a higher population. Therefore we experiment with different numbers of connections in a specific pattern. Although different types of movement are required, the lacking of reconfigurability in the joints while trying to achieve that variety, results in a decrease of reorganisational qualities in the system.

As it is expected from the system to create local packs in order to achieve its goals, it is important to define how connectivity is achieved in the system. To have the flexibility the system requires, it is useful to have two different joint types. The one type is fixed and all the other behaviours occur depending on the joint, whereas the other is flexible and allows agents to adjust their connections.

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MATERIALISATION - MOBILITY

For the systemâ&#x20AC;&#x2122;s development, it is important to achieve an organisational model with higher population. Therefore we experiment with different numbers of connections in a specific pattern. Although the observations indicate different types of movement can happen, the lack of reconfigurability in the joints prohibit the system from reorganising itself.

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These results are achieved through pre-defined patterns. Less connections result in less dependency from the neighbours and less control over the system, while more connections result in more densely packed and less mobile systems.

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MATERIALISATION - MOBILITY

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MATERIALISATION - MOBILITY

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MATERIALISATION - MOBILITY

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MATERIALISATION - MOBILITY

STRUCTURING

MIN AMOUNT OF UNIT THAT THAT CAN CAN MINIMUM AMOUNT OF UNITS CREATE A STRUCTURE

TRACTION

ADDITION

SIGN

CREATED MINIMAL PACK

STEP 1: CREATING THE NEIGHBOURHOOD CONDITIONS

STEP 2: STEP 2: UNIT STARTS TO LEANING MIDDLE THE MIDDLE UNIT STARTS LEARNING ON TOP OF ANOTHER WHILE ONOTHER TOP OF ANOTHER ONEANOTHER ADJUSTSWHILE ITS SHAPE ONE ADJUSTS ITS SHAPE

SUPPORTING THE STRUCTURE

STRUCTURING

STEP 3: AFTER THE POSITIONS HAS BEEN TAKEN INBETWEEN TWO UNITS THE THIRD ONE PUSHES IT ON TOP

STEP 4:

BY STRETCHING FROM THE BOTTOM TWO MIDDLE UNIT GOES ON TOP

SUBSTRACTION 206

UCTURING THROUGH ADDITION

UNIT DESIGN UNIT DESIGN

THE THIRD ONE PUSHES IT ON TOP

ON TOP

UNIT STARTS TO ACTIVATE INCREASING TENSION UNIT STARTSTHE TO ACTIVATE BETWEEN CONNECTED UNITS INCREASING THE TENSION

BETWEEN CONNECTED UNITS ACTIVATED UNIT RELEASED ACTIVATED UNIT RELEASED

UNIT DETACHED FROM THE PACK UNIT DETACHED FROM THE PACK

ADDITION ADDITION

E

E

SUBSTRACTION SUBSTRACTION

INFLUENCED BY THE NEIGHBOURS PUSHED TO TOP INFLUENCED BY THE NEIGHBOURS PUSHED TO TOP ADJUSTING ITS POSITION AND SHAPE TO FIT IN WITH THE NEW NEIGHADJUSTING ITS POSITION BOURS AND SHAPE TO FIT IN

WITH THE NEW NEIGHBOURS BOTTOM UNIT STARTS TO PUSH OTHERS BOTTOM UNIT STARTS TO PUSH OTHERS RESTRUCTURING ACHIEVED BY ADDITION TO TOP LAYERS RESTRUCTURING ACHIEVED BY ADDITION TO TOP LAYERS

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MATERIALISATION - MOBILITY

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MOBILITY - MACHINE LEARNING One Directional learning

Actions Extracting for Prototype

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Multi Directional learning Branch Activation Order

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MOBILITY - MACHINE LEARNING

Multi Directional learning Dynamic Target Reaching

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Multi Directional learning Dynamic Target Reaching - Multiple Agents

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MOBILITY - MACHINE LEARNING

Multi Directional learning Dynamic Obstacle Avoidence

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Multi Directional learning Dynamic Target Reaching - Multiple Agents

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PROTOTYPE

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PROTOTYPE

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PROTOTYPE

structure Joint Custom made joints to attach to the solenoid cases. Mechanical connections are created to keep the core structured and in place.

Arduino Uno / Arduino Mega Processor

Custom Designed Solenoid Case To create a core for all the electronical system and attach solenoids accordingly to the structure a case designed MotherBoard and Case Custom made mother board to enable communication with devices and control of the pneumatic cylinders

Solenoid Valve Solenoid valves are used for each pneumatic cylinder in order to control the activation order

Custom Design Ball Joint Ball pieces attached to each other in a certain angle to keep everything in a certain angle to protect the shape

Manifold Piece Distribution of the air pressure to the solenoid valves 12 outputs for each solenoid valve

Custom Design Ball Joint Claws designed to be screwed to the pneumatic cylinders with mechanical connection.

Direction to fit all joints to keep the geometry in activated or deactivated state

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PROTOTYPE

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PROTOTYPE POROUS SILICONE (AIR GAPS) pouring process: silicone(ecoflex50) + inflated hydrogels

SKIN

PRO - V

TO SURFACE

TO EACH OTHER UNIT 1 UNIT 2

PRESSED TO SURFACE BY THE SKELETON INFLUENCE

POROSITY OF THE SURFACE CREATES SIGNIFICANT AMOUNT OF FRICTION UNIT 1 UNIT 2

SUCTION CUP EFFECT FORCE RELEASES THE AIR WITHIN THE SILICONE GRIPPER EFFECT

RESOLUTION

STATE CHANGE 224

DOUBLE USCTION EFECT CREATES STRONG FIXED CONNECTION

porosity: extraxtion of hydrogels

STATE CHANGE STATE CHANGE

STATE STATE 1

STATESTATE 2 2

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sed diam nonummy nibh euismod tincidunt ut laoreet

Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet

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PROTOTYPE

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PROTOTYPE

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PROTOTYPE

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PROTOTYPE

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PROTOTYPE

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PROTOTYPE

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PROTOTYPE

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PROTOTYPE

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PROTOTYPE

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SCENARIO

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SCENARIO

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SCENARIO

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SYSTEM SCENARIO SPACE TYPE: LANDSCAPE

Deforming Playful Space

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SYSTEM SCENARIO SPACE TYPE: ENCLOSED

Enclosed Space

Roofed Enclosed Space 252

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SYSTEM SCENARIO SPACE TYPE: URBAN EQUIQMENT

Human Scale Furniture

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SYSTEM SCENARIO SPACE TYPE: PATHWAY

Arch Space

Spatial Separation 256

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EPILOGUE

We believe that culture is what most of the people do most of the time.

In our current built environment what we do is defined by the city that we inhabit, while our routines are determined by the opportunities and obstacles created by the urban infrastructures. Therefore, 4057 introduces a new system within the existing infrastructure systems, which interacts with the inhabitation routines of the cities, in order to redefine them, thus redefining urban culture.

The existing infrastructure is defining our routines and culture in a static, non-dynamic way, that is not possible to adapt and interact with the user and his needs.

Therefore our system, allows the habitat to respond to the userâ&#x20AC;&#x2122;s needs by its emergent behaviours and organisational abilities in order to create cities that are intelligent, interactive time-based space generators.

4057 wants be part of the a revolution within communication, computation and technological progress that architecture has witnessed in the last years. 4057 wants to contribute to the debate on how our cities can become adaptive and how to overcome the static role architecture plays in the current urban infrastructure. We do not believe that the transformation of the city can be a goal achieved by only one way, or one project. This task belongs to the new generation of architects, researchers and designers, the ones that will approach architecture as an infrastructure and technology as culture, that will doubt the status quo in architecture and will try to conceptualise and realise the architecture of the future.

Because architecture can only exist by adapting to the cultural changes that take place in our world, either technological, ecological or social, thus being efficient in co-constructing solutions for the society and its people.

ACKNOWLEDGEMENTS

FROM LEFT TO RIGHT MOSTAFA EL-SAYED, QIQUAN LU, OGULCAN SULUCAY, LARA NIOVI VARTZIOTIS, ECE BAHAR ELMACI BADANOZ, THEODORE SPYROPOULOS

First and foremost we would like to thank our families and loved ones for supporting us in our work, in the good and the bad times during our studies in the Design Research Laboratory of the Architectural Association.

We want to especially thank our tutor and DRL director Theodore Spyropoulos for his constant support and for always being there for us. His presence gave us the motivation to overcome every dead end and finally bring our project to life.

Special thanks go also to the founder of the DRL, Patrik Schumacher, and the course masters of the Design Research Laboratory, Shajay Bhooshan and David Greene for all the feedback and inspiration. And of course Ryan Dillon, the coordinator of the DRL Programme, for all the great and helpful organisational work he did.

This project could not happen without the great support and consultation of Apostolis Despotidis and Mostafa El-Sayed as well as our other course tutors, Alicia Nahmad Vasquez and Pierandrea Angius and our technical tutors from AKT II Albert Taylor and Ed Moseley.

We want to also thank the Architectural Association and the Design Research Laboratory for offering us the opportunity to be part of the team and for introducing us to a new world in architecture, as well as the Digital Prototyping Laboratory and Angel Fernando Lara Moreira and Henry Cleaver for providing the technical infrastructure to make our project come true.

Many thanks also go to the phase 1 students Andrew Friedenberg, Giulia Arienzo Malori, Edward John Hamilton Meyers, Shiri Dobrinsky and Elizabeth Konstantinidou and everyone else who helped us in times of pressure, as well as our classmates, for sharing with us this incredible journey in architecture and research.

To all these people, we are sincerely grateful.

Lara Ece Ogulcan Qiquan

Architectural Association School of Architecture AA School Design Research Laboratory

AA School Design Research Laboratory Architectural Association School of Architecture