Adaptive Ecologies Workshop Report
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INTRODUCTION
PROGRAM AND OVERVIEW
METHODOLOGY
SOFTWARE PLATFORM
ITERATIVE NEGOTIATIONS AND EMERGENT COMPLEXITY
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EMERGENCE IN AGENT-BASED SYSTEMS
DESIGN EXCERCISE
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Over recent decades the city of Dubai has experienced an unprecedented process of development and exponential growth, bursting from a small town into the metropolis we know today. This evolutionary capitaldriven expansion, catapulted by a determined push to prosperity, ruled out the realization of a global and unified master plan.
Dubai is now an icon of futurism and opulence. The modern setting of Dubai is, however, directly adjacent to the organic fabric of its old town, creating a striking contrast in behavior and locale. Whilst the old town expanded in an organic manner from the inside out, the new Dubai is spreading in an orderly fashion from the outside in. This stark disparity between the two zones has resulted in a diversity of urban spaces. Â The current growth is gradually getting
integrated into the existing fabric of the city, filling scale gaps within the new region and its connection to the old one. Whether directed outward or inward, growth is a characterizing, unique element worth investigating in the urban design of Dubai. EXPOn-tial is an Advanced Grasshopper workshop aims to explore the potential of growth processes for their pertinence to urban scale phenomena. Natural growth processes share common traits similar to computer growth algorithms such as branching systems, diffusion
limited aggregation and other rule-based processes. Participants developed nonlinear growth strategies whose spatial results and forms can be evaluated across a wide range of scales - from material to urban. Behavior of growth processes depends largely on material organization properties (geometry and force) as they will influence clustering and distribution across an environment. The systems were developed by assessing their potential character on a basic level of organization first, then developing it to work on adaptation processes of autonomous, coherent objects that have manifold potential on a variety of scales.
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The five-day workshop program was designed to introduce participants to the principles of non-linear iterative processes such as Agent-based system, Branching system, and DLA (Diffusion Limited Aggregation), then to explore their application in a selected design exercise of their choice. Non-linear iterative processes are new ways of integrating computation in the design process. They suggest a possibility of playing out a whole range of iterative negotiations in the process of establishing a design system that allows a completely different hierarchy of design decisions to emerge.Â
The workshop had a prerequisite that participants should already posses a basic grasshopper knowledge. Therefore the tutoring program kick-started with advanced exercises on lists manipulations and data tree management to build up on their beginners’ level with more complex techniques. The first three days were spent learning the fundamentals of working with iterative system in grasshopper and Anemone and establishing logic of definitions, building and managing loops structure, and developing datadriven system behaviour. The aim was to introduce
participants to multiple techniques to develop rulebased growth systems with a variety of design outcomes. System behaviour was explored through multiple iterations of a certain rule within an applied force field. Participants learned how to develop complex systems from the iteration of a simple rule. On the afternoon of the third day, participants formed four groups; each group presented a design concept that was discussed regarding its potential and development direction. The groups then explored and developed their concepts over the next two days.
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The workshop explored the generation of different iterative growth systems and evaluated their application in architecture and the growth of an urban fabric. After covering few exercises on the logic and the principles of this design medium, participants applied
the learned techniques in a design exercise of their own selection. The workshop steered away from directing the design exercise to a specific agenda and left the participants at liberty to explore their own subject and to discover the potential of these
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Through the use of looping systemw in Anemone, participants learned how to develop complex systems through multiple iterations of a simple rule. Each group selected a different approach for the application of these tools in their project. Different outcomes were developed in response to different design agendas that were derived from a manifold of information. processes. After deciding on the concept, participants selected different autonomous behaviors and data-sets for the environment that were later translated into iterative rules and negotiation protocols that defined their systems.
Growth processes can be described as of a generative nature. The generative design process is initiated by defining a number of basic rules, a set of parameters, and an environment. Although the rules are played out
in sequences of simple operations, the outcomes always exceeds what is inscribed in the rules themselves. This might happen in more than one way, from the global emergent effects (wholes that posses properties that cannot be traced to the single parts) to intensive transformations leading to a heterogeneous map of singular conditions rather than detectable components. One or more of these results can then directly become part of the design, or the starting point for a new process with a different set of rules and techniques. In both cases, the designer must understand the system and act on the rules, the parameters and
the environment in order to regulate the design. The focus thus shifts from a prefigured final design towards the definition of fundamental ingredients that affect the form generation process. This self-organizing method is understood as a selfregulating mechanism, where individual parts of the system interact through negotiation, thereby displaying ordered behaviour.
mathematical significance. It refers to the existence of an underlying logic or system that is recognizable and therefore, in principle, possible to describe through a set of rules.
On the technical level, participants learned how to compose, edit, and generate rule-based iterative algorithmic definitions using Grasshopper and other plug-ins. On the design implementation level, they The growth processes that we have been supervised and experimented with in this assisted to prepare the workshop produced spatial logic and structure of patterns. Here, the term their own definitions, with pattern is not limited to grasshopper and with other the general use of the word plug-ins, and to develop them as a decorative element, but to reflect their particular relates to its spatial and design intent.
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Rhinoceros 5 and grasshopper were used as the main software platform, along with few other GH plugins mainly Anemone (loops generation), Weaverbird (mesh editing, subdivision, mesh transformation), Meshedit, The workshop technical tutorials included: Perlin noise, Sandbox topology, and Ghpython. __ Lists & Data Tree: management, manipulation, and visualization. __ Mesh creation, subdivision, and manipulation. __ Forcefield (Gradient Attractor field with multiple forces) __ Anemone, Introduction to loops and iterative system. __ Advanced exercise: Random Walker, Perlin Noise, Lloyd Algorithm. __ Advanced exercise: Marching Cubes (Isosurfacing), DLA aggregation, Mesh Drainage, Agent-based algorithms.
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Generative design processes provide means for addressing complexity in architectural design. One type of complexity is concerned with the evolution of certain geometry. Another
type is concerned with the possibility of establishing continuous negotiations between design parameters within the form-generating process. Design parameters, which are normally adjusted through trial and error experimentation, can be embedded in the formgenerating process. Thus, a continuous negotiation of these parameters is taking place as the design scheme develops from the initial design intent through to realization.
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In the design process, designers often have to establish hierarchy in the selected parameters by allocating a few parameters as primary while leaving others to become secondary. In this workshop, a field of negotiation is established and manipulated in the form-generating process. Through the development of the system, participants became aware of priorities and indirectly started to guide the system towards particular solutions. The role of the designer thus shifted from controlling the outcome to guiding the
process to possible outcomes. The final result represented the outcome of numerous complex negotiations between essential parameters and design intents as set by the designer. In generative processes, negotiations are rooted in the basic rules that are set to communicate the design intent. The design approach within this workshop demonstrated how design intent can be expressed through the development of algorithmic logic. For instance, self-organizing particles in the DLA system
exercise demonstrated that the spatial configuration of the particles and the underlying algorithmic logic are fundamentally connected. Although the design exercises were not directed towards actual design solutions, the methods are generally developed to a level where they can adapt to an architectural or urban context. As mentioned earlier, growth processes are inherently iterative, rule-based, and non-linear. The processes are nonlinear because they enable negotiations
between several parameters to be played out during the form-generation process. In this sense, nonlinearity is perhaps an inherent property of self-organizing systems, rather than an advantage. It is a property that enables the development of systems integration as part of the design process. Design intents are refined through feedback in the form of the behaviour of the nonlinear system, which changes through the development process. Contrary to linear processes where the outcome is
directly linked to a certain parameter or variable in the logic, iterative negotiations in nonlinear systems produce different degrees of adaptability. The workshop demonstrated that, through the use of a relatively simple mathematics, a large degree of complexity could emerge through numerous iterative calculations. The unpredictability of the result is thus not a primary property, but rather a consequence of the complex negotiations in the generation of agent-based systems.
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Agent-based systems exist in a variety of formats in nature such as living systems and physical systems. They consist of numerous autonomous entities that interact through a basic set of rules on a local level to generate global behaviours. Development of digital computation allows the generation of models to consist of a large number of
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both agents and iterations, thereby enhancing the possible complexity of the system. It becomes possible to simulate behavior of birds flocking, school of fish, colonies of bacteria, or the construction of ants’ mounds. A large quantity of agents and their interaction over a number of time frames is necessary for the generation of these emergent effects. One particular aspect about agent-based systems is that the behaviours displayed by the system are not connected directly to specific parameters. They are neither coded nor designed, but rather they emerge out of complex negotiations in the iterative process. Emergence
can then be understood as the appearance of order on one level, generated from entities that selforganize on a lower level. This type of order is not the result of explicit topdown control, since the entities are guided by internal properties and interaction with each other and their surroundings. This is similar to the way cells in an organism behave and form a well-functioning living system like a plant or an animal, or termite behaviour that is influenced by pheromones. In nature, emergence also appears in cases that would normally not be said to be agentbased. Examples of patterns occurring from material properties are cloud
formations, sand dunes, dendrites and soap bubbles. The difference is that, the entities in these systems lack the capability of ‘acting’ and are subject to external forces. The entities in these systems are hence considered passive, since they lack independent control over their own behaviour. Agent-based systems occasionally operate with such entities, and are subsequently addressed to as ‘passive agents’. Typically, a system dealing only with passive agents is called a particle system. John Holland in his book “Hidden Order, How Adaptation Builds Complexity” described agent’s behaviour as being
determined by a collection of rules. This is typically a stimulus-response rule: ‘IF stimulus “s” occurs THEN give response “r”. The system’s performance is then the result of a succession of stimulus-response events. Holland discusses some of the properties and mechanisms relevant to agent-based systems, such as aggregation, emergence, nonlinearity, flow, diversity and tagging. According to Holland, Emergence refers to the ability of the agents in the system to form ‘complex large-scale behaviours from the aggregate interactions of less complex agents.’ A large quantity of agents and their interaction over a number of time frames is necessary for the generation
of emergent effects. Nonlinearity addresses the fact that there is no direct way of predicting the outcome of the system through an analysis of its logic and environment. Physical systems, such as the formation of sand dunes, waves, or clouds, can essentially be described through physical laws that are affecting the particles. In biological systems, the complexity of the living components is much higher, as exhibited through ant colonies, neurons and bacteria. Here, the ability to develop new behaviours and refine the interaction between the agents through natural selection is a crucial aspect.
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On the fourth day, participants were divided into four groups to develop their own concept. The groups explored natural systems as a reference for their pattern’s growth behavior and investigated their potential in relation to their design intent. The design intent was guided away from the production of a single outcome, with
a deeper focus on the process of generation and adaptation. Consequently, the exercises were focused towards the mechanisms that generate these patterns rather than the emerging patterns themselves. Instead of mimicking patterns found in nature, the developed methods can be adopted for generating new types of geometric organization capable of expressing the design intent set by each group. Traditional design processes establish
hierarchical relations in many ways. This can be seen as hierarchy between structure and cladding, envelope and program, material and form and many others. The use of generative techniques suggests a different understanding of ‘hierarchy’. Rather than being imposed, hierarchies emerge within the form-generating process as the result of numerous iterative negotiations. The findings of the design exercise demonstrate that the use of generative growth techniques leads to a shift in perspective with respect to the hierarchies and possible outcomes of the design process.
The groups demonstrated the potential of such systems in producing a coherent design system based on behavioural approach of the elements in relation to the forces of certain contexts. The design exercise was an exploration that looked at the ability of the macro order to emerge from the interaction of components at a local level. Emergent hierarchies were produced as a result of the employed mechanisms that allowed agents to selforganize within the system. The final presentation by each group represented a work in progress state in the generation of a selforganizing system.
The explorative feedback process allowed participants to break away from the traditional linear design methodology and arrive at a more bottomup oriented approach to generate possible forms and spatial patterns. As these processes are behavioral at their core, they allow information to organize into a coherent system able to produce heterogeneous spatial outcomes of increasing complexity from simple sets of basic rules. This systematic variety opened up the design space towards new potential forms of correlation between behaviorbased spatial systems and urban ecosystems.
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EFFUSIVE ERUPTION GROUP MEMBERS Maitha Khalifa AlMazroei Rola Dehm Suha Al Salamain
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BEHAVIOURAL RULES
[
Mesh > Subdivision
YES
generate new particles + layering the strata
to generate particles
NO keep on moving
1
Geometry: provides a constrain and influences the process
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Particles Generating Curves along the Geometry
Connect Points to form polylines
end loop
start loop
INSPIRATION
]
Generate Field move particles along the mesh Condition
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5
Particle Generation from the Geometry mesh vertices
Condition Applies: particles move up along the normal direction of the mesh and generate a new Layer of curves
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Particles flow along the Geometry
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Stratification of the generated layers
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STUDY 04
STUDY 03
STUDY 02
STUDY 01
CATALOUGE OF STUDIES
Mesh Pipe r = 0.6
Isosurface Mesh Resolution = 0.4
Isosurface Mesh Resolution = 0.3
Curves
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CRYSTALLIC SYMBIOSIS GROUP MEMBERS Taufeeque Abdul Marta Krivosheek
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CRYSTAL STRUCTURE
CRYSTALLIZATION
highly ordered structure occurring due to the intrinsic nature of molecules
addition of new atoms, ions, or polymer strings into the characteristic arrangement
crystal lattice - unique arrangement of atoms or molecules (unit cells) lattice parameters - lengths of the edges of a unit cell and the angles between them
growth is fixed on positions in space relative to each other; spreads outwards; homogeneous (without the influence of foreign particles) or heterogeneous (with the influence of foreign particles)
gallium arsenide (GaAs) crystal structure
ADOPTED LOGIC CRYSTAL GROWTH seed crystal
INSPIRATION
growth defined by directional vectors (“tripod”) [ angle + distance from seed crystal ]
crystal growing [ L-system ]
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SYSTEM RULES BEHAVIOUR 1 _ DIRECTION OF GROWTH
BEHAVIOUR 2 _ CONNECTIONS
approaching particle
minimal distance and minimal angle evaluation
BEHAVIOUR 3 _ PARTICLE MOVEMENT & AGE particle 1
new particle
[
particle lifespan (age) max. number of cycles
lattice distance evaluation
shortest connected
SYSTEM DIAGRAM FOR ALL BEHAVIOURS Generate Seed Crystals
doesn`t trigger growth == terminated
force field for the particle movement
conditional crystal growth
Randomly Generate Particle
Generate Force Field (FF)
start loop
Move Particle in the 3D FF Particle Distance to Closest Seed / Crystal
NO Keep Moving
Yes Evaluate Behaviour 1 & Grow Evaluate Behaviour 2 & Connect
]
Check Particle Age
<max >max
Terminate
Generate New Particle
end loop
Crystalline Structure
possible paths of growth restricted to three (3) specified points in space
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Growth from one (1) Seed Crystal Directed by Behaviour 1
01 _ seed crystals and force field charges
02 _ force field particle trajectories
03 _ growth from seed crystals
Growth from One (1) Seed Crystal Directed by Behaviours 1 and 2
Growth from Multiple Seed Crystals Directed by Behaviours 1, 2 and 3
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AGGREGATION OF MAGNETIC FIELDS GROUP MEMBERS Amna Al Owais Omar Kaddourah Nada Radwan
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EXTRACTED LOGIC
Earth’s gravitational pull
magnetic attraction
INSPIRATION
Composition of atoms
PROCESS
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01_
01_
02_
02_
04_
03_
03_
04_
05_
06_
defined boundary
generated particles
random numbers
check collision of particles
YES magnetic field
charges with different influence & radius
random radius check boundary boundary limit
NO
aggregate keep on and generate moving new particle if growth doesn`t exceed particle will pass
LOOP End
stationary charged points in boundary
LOOP Start
SYSTEM DIAGRAM
particles
charges
create field lines
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INVASIVE SYSTEMS GROUP MEMBERS Reem Hantoush Ahmed Shaikhon Yasmeen Khalil
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INSPIRATION PROCESS LOGIC Start IN.VA.SIVE
TENDING TO SPREAD PROLIFICALLY AND UNDESIRABLY OR HARMFULLY
Distance to Closest Attractor
Hits Boundry & Reflects
Clusters hit Attractor & Muliplies
Medium of Interaction
Stationary Triggers
//Environment
NUCLEAR FISSION
LIQUID BINARY FISSION
2 TRIGGERS ACT ON PARTICLE
BOMB EXPLODES
Moving Particles
//Fungus
ABLE OR LIKELY TO SHATTER VIOLENTLY OR BURST APART, AS WHEN A 1 PARTICLE MOVE TOWARDS TRIGGER
EX.PLO.SIVE
DEAD ANT INVADED BY FUNGUS
Close Enough
//Ants
3 PARTICLES DOUBLE // EXPLOSION GROWTH
ROOTS GROWTH
Far Away
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Tutors Zayad Motlib - d-NAT Alessio Erioli - Co-de-iT member & co-founder
CREDITS
Report prepared in collaboration with Zayad Motlib, Marta Krivosheek & Alessio Erioli Edited by Zayad Motlib Graphics by Marta Krivosheek
Participants Taufeeque Abdul Maitha Khalifa AlMazroei Amna Al Owais Suha Al Salamain Rola Dehm Reem Hantoush Omar Kaddourah Yasmeen Khalil Muhamed Khalid Marta Krivosheek Nada Radwan Ahmed Shaikhon
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