NSS.AA midterm

// NSS.AA

Natural System Studio_ Aiman Ahmad

Natural System Studio; Aiman Ahmad 397733

//agents + interactions

CONTENT

//AIMAN AHMAD WEEK 07 PARTICLES SYSTEM _ NATURAL SYSTEM STUDIO//PROCESSING CONTENT;//The project is about how the agent interacts with each other, //transform their polygon shape and size; getting smaller and expand//the group of agents will end their journey at the periphery of the window//less group of agents will follow the big group one//the direction of a singular agent is random


Simplicity and Complexity ; Reflection

Nature is frugal, uses the simplest thing, repeated rules to form a complex sys-tem; Computational theory book.

As what referred to the computational theory books, nature is frugal, which its uses the simplest thing to form a complex system. Starting with the simple behaviour of one agent, the system operates by repeating the same rules to form a complex system. The simplicity and complexity of how universe works can be look at one simple example, the ant’s colony system. It is important to not predetermined the natural system and look it as an ordinary thing. It is merely more than that. By looking at how ant behaviour interacts within their group, scientist has identified the complex and sophisticated pattern emerged within those interactions. As what cited from the computational theory book, such examples i.e. economic markets defy predictions, human immune’s system and evolution of our planet; are emergent of one simple units and when combined form a complex whole.

image credit; Computational Theory_Lindenmayer sys-tems and the Computational Beauty of Nature, fractals.

// Readings


Make A Hybrid_Swarm Architecture in Almere Netherlands

I find this project similar to what I want to achieve in my project to the scope of they use points as agent to create spatial character and their struc-tural system. In this project, parametric model was used to defining the space. The spaces are created through the bisectors of any adjacent points. The components have variations in their shape and size which the points coordinate and positions were the influence of their character. The behav-iour of each point was derived from the data that they have collected. The project respectively used the parametric approach in their design since the data will determine their design decisions. The points are the design generator which leads to multiple possibilities of design outcomes. To re-alise this project, the structural system comprises of the triangulated truss system. The truss system is a self-supporting system which enables each component structurally supporting each other. [1]

1. Make A Hybrid_Swarm Architecture in Almere Netherlands

// Precedent Study


Make A Hybrid_Swarm Architecture in Almere Netherlands, Structural DetailsImage credit: http://www.makeahybrid.org/2010/10/swarm-architecture.

Make A Hybrid_Swarm Architecture in Almere Netherlands, 3d renderingImage credit: http://www.makeahybrid.org/2010/10/swarm-architecture.

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// Precedent Study


In this topic I want to cite two articles which used swarm intelligence i.e. flocking system in their project. The first article is written by Jonas Sin. In his project he justified the ‘fibrous structure’ as the fundamental of his design out-comes. ‘Fibrous Structures aimed to explore the potential for design innovationusing parametric modeling tools and fiber-reinforced super-fluid concretes.’ The fibrous struc-ture explores the potentials of line- to surface technique in creating spatial character and structural possibility. The line could mesh together with the surface to create a nets system, theoretically a minimal surface in generating design outcomes. [1] He also explained that the design outcomes are solely from one design generator i.e. fibrous structure, and not a result of the combination of different part or compositions.

The second article was written by Pablo Miranda Carranza from University of East London. Partly in his writing he em-phasizes on the flock algorithm as the essential element in understanding the swarm intelligence. He described the swarms system as the examples of sensori-motor intel-ligence. He also emphasized the beauty of swarm system through the simplicity of its mechanics and its complexity as phenomenon. In his project he explored the relation of agents with obstacles. The behavioural models include the agents to fly through simulated environments while avoid-ing the obstacle. The traces of agents have been docu-mented and the result is the smooth trajectory derived from the agents movement and its behaviour of avoiding obstacles. [2]

1. Make A Hybrid_Swarm Behaviour. Jonas Sin. http://www.makeahybrid.org

2. Swarm Dynamics_ Pablo Miranda Carranza, Uiversity of East London. http://www.generativeart.com

// Precedent Study


// Precedent Study

Make A Hybrid_Swarm Behaviour. Jonas SinImage credit: http://www.makeahybrid.org

Swarm Dynamics,Traces of Agents_ Pablo Miranda Carranza, University of East LondonImage credit: http://www.generativeart.com

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The Explosion: Sequential process_ Flocking multi-agent model system: 400

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// Swarm Intelligence: Flocking


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Original Code credit to:Lyndon Manuel, OpenProcessing 2012

I have found one code in Open Processing by Lyndon Manuel which uses flocking behaviour in its coding system. I did some explorations with the code and begin to extend and experiment with the effects. The outputs have been documented in this journal for my academic purposes.


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Agent Properties:100 agentsVector: Width/2, Height/2Cohesion value : 1.0 (high)Separation : 20( low)

Agent Properties:500 agentsVector: Width/2, Height/2Cohesion value : 0.001 (low)Separation : 200( high)Align : 0.05( low)

Separation: ‘Gives an agent the ability to maintain a certain sepa-ration distance from others nearby. This prevents agents from crowding to closely together, allowing them to scan a wider area. To compute steering for separation, first a search is made to find other indi-viduals within the specified neighbourhood. For each nearby agent, a repulsive force is computed by sub-tracting the positions of our agent and the nearby ones and normalising the resultant vector. These re-pulsive forces for each nearby character are summed together to produce the overall steering force. ‘[1]

Cohesion: Gives an agent the ability to cohere (approach and form a group) with other nearby agents. Steering for cohesion can be computed by finding all agents in the local neighbourhood and computing the “aver-age position” of the nearby agents. The steering force is then applied in the direction of that “average posi-tion”. [1]

Alignment:Gives an agent the ability to align itself with other nearby characters. Steering for alignment can be computed by finding all agents in the local neigh-bourhood and averaging together the ‘heading’ vec-tors of the nearby agents. This steering will tend to turn our agent so it is aligned with its neighbours.[1]

1 Swarm Dynamics_ Pablo Miranda Carranza,

Uiversity of East London. http://www.generativeart.com

Agent Properties:500 agentsVector: RandomCohesion value : 0.1 (high)Separation : 200( high)Align : 0.05( low)

Agent Properties:500 agentsVector: RandomCohesion value : 0.1 (high)Separation : 200( high)Align : 5( high)

// Flocking behaviour Cohesion, Separation, Align.

Sequential process_ Flocking multi-agent model system: 100 agents

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Traces of Agents_ Flocking multi-agent model system: 400 agentsMaster Image:

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// FIBER PATH Traces of Agents. Structural Network. Branches


Connecting _ Flocking multi-agent model system: 400 agents

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Sequential process_ Flocking multi-agent model system: 20 -100 agents

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// THE FLOCKING BEHAVIOUR void flock(){ separate(20); a certain separation between agent cohesion (0.001); //cohesion(0.001) the agents unity, high the value high the attraction align(0.5);//align(.5) agents allignment with each other

AgentCollection = new ArrayList(); //agents quantity for(int i = 0; i < 5; i++) { //control the number of agents //the agents origin and the sprawl randomnes // VECTOR PROPERTIES Vec3D x = new Vec3D(random(width),random(height),0); Agent myAgent = new Agent(x); AgentCollection.add(myAgent);

THE FLOCKING BEHAVIOUR void flock(){ separate(20); cohesion(1); //cohesion(0.001) the agents unity, high the value high the attraction align(0.5);//align(.5)agents allignment with each other // VECTOR PROPERTIES AgentCollection = new ArrayList(); //agents quantity for(int i = 0; i < 5; i++) { //control the number of agents //the agents origin and the sprawl randomnes // VECTOR PROPERTIES Vec3D x = new Vec3D(random(width),random(height),0); Agent myAgent = new Agent(x); AgentCollection.add(myAgent);

In order to fully understand the potential of agent and the behaviour, I have minimised the number of agents into 5 only. Then, the emergence has being documented. The result is more predictable. With only 5 agents in the system, the parameters between each agent are more constant. Thus, less confliction because of less crowding and yet more predictable outcomes emerge from system.

‘Everything should be made as simple as possible, but not simpler’[1]

1. Albert Einstein. www.alberteinsteinsite.com/quotes/htmlFor photos 3-6, the exploration of the behaviour begins with 20 agents in the system. The vector properties are in a random value. Thus out-comes are more dynamic and less predictable.

At this stage, the cohesion value has been increased. Therefore, agent’s interaction between each other increases and as well their ability in forming groups. The emergence of nodes which are the group of



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1-4// Properties(exploding effect):The code with vector properties(i.e. Vec3D(width/2,height/2,0).Thus, the more symmetrical behaviour have been identified in the system.Experiment with 50-200 agents, speed limit (20)// Trace of Agentsif (distance > 0 && distance < 300){ //increase the trace distance between each points to 300, thus the complexity of traces increase in the system.

//Properties(exploding effect):The code with vector properties (i.e. Vec3D(width/2,height/2,0).Thus, the more symmetrical behaviour have been identified in the system.Properties(exploding effect):Experiment with 50-200 agents, speed limit (20)//Flocking behaviour; void flock(){ separate(500); // cohesion(0.25); //Increase the cohesion value, the behaviour become more compress align(0.5);//align(.5)agents allignment with each other} // Trace of Agentsif (distance > 0 && distance < 300){ //increase the trace distance between each points to 300, thus the complexity of traces increase in the system.

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The Explosion, sequential process_ Flocking multi-agent model system: 400 agents

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//Properties(exploding effect):The code with vector properties (i.e. Vec3D(width/2,height/2,0).Thus, the more symmetrical behaviour have been identified in the system.Properties(exploding effect):Experiment with 400 agents, speed limit (20)//Flocking behaviour; void flock(){separate(500); //cohesion(0.001); // Decrease the cohesion value, the behaviour become more com-pressalign(0.5);//align(.5)agents allignment with each other} // Trace of Agentsif (distance > 0 && distance < 80){ // decrease the trace distance between each points to 80. This is because to avoid conflictions with the increasing number of agents


The Explosion, sequential process_ Flocking multi-agent model system: 400 agents

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More organised behaviour emerged from the system. The pattern is more predictable and less complex.


//5-20agents ; identify the emergent of the system_The relation of no of agents with cohession value______________ //flock behaviour THE FLOCKING BEHAVIOUR void flock(){ separate(1000); cohesion(1); //cohesion(0.001) the agents unity, high the value high the attrac-tion align(0.5);//align(.5)agents allignment with each other}// increased the distance to 100void cohesion(float magnitude){ float distance = loc.distanceTo(other.loc); if (distance > 0 && distance < 100){ //if (distance > 0 && distance < 20){ sum.addSelf(other.loc);


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The emergent_ Flocking multi-agent model system: 5 agents

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//5-20agents ; identify the emergent of the system_The relation of no of agents with cohession value:RESULT = AGENT MORE LIKELY TO FORM A GROUP WITH EACH OTHER______________ //flock behaviour THE FLOCKING BEHAVIOUR void flock(){ separate(1000); cohesion(1);

//cohesion(0.001) the agents unity, high the value high the attraction align(0.5); //align(.5)agents allignment with each other}// increased the distance to 100void cohesion(float magnitude){ float distance = loc.distanceTo(other.loc);


At this stage, I have problems in connecting all the agents and es-tablishing the stable trajectory. The trajectory (path) seems breakable between the agents. This happened because of the low value of distance between each agent in order for them to create a path(i.e. if (distance > 0 && distance < 150){ //increase the trace distance between each points).

The cohesion of the agents is low and the separations are high. In or-der to establish firm structural net-works, I have to reduce the random-ness of agent behaviour.

The behaviour becomes more complex. The paths grow between agents.While the distance between the agents is increased; they have creat-ed the trajectory between them. This is because the distance value is high. (i.e. if (distance > 0 && distance < 300){ //increase the trace distance between each points).

I have to reduce the complexity of the behaviour, so as to achieve a much more stable structural net-work.



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Master Image

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At this stage, I have set the Y vector to be at the middle of the environment (i.e. Vec3D(random(width),height/2,0) . Agents have appeared in a more organized way. The flocking behaviour begins to expand and established traces of agents. From this analysis, I have recognised the emergent behaviour of interaction between flock members have potentials in producing the branching of structural systems. ‘In a form finding process, these spatial charac-teristics can also arrange structure. The next step is to understand the sequence of connecting the nodes’.[1]

1.Michael Hensel & Michael Weinstock, Emergent Technologies and Design 120. (Routledge : 2010)

Emergent of branching structural system_ Flocking multi-agent model system

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Sequential process_ Flock of people - Agent-Traces

Traces of Agents _ Potetial cables nets structure as the emergent of technologies ad design

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Flocks of PeopleImage Credit: http:richardcoyne.com

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From the flocking behaviour system that I have explored, I can see the potential of the system in integrat-ing it to the architectural discourse. The agents (points) have the ability in organising spaces within their respective environment. The spatial characteristics can also defining the structure system. One of the examples is the nets structure.The nets are defined by the network topology. The network topology is based on a ‘ring’ network method of association. The potential of network topology in architecture is the emergence of multiple spaces defined by a single continuous boundary. Node is where the connections of the cable (structural connec-tions). The node identification is the most essential information of the system. [1]

1. Emergence seminar research by Sea Ahlquist and Moritz Fleishmann, 2008.


Flocking Behaviour_ Ratio 10 - 500 agents

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Fibre Path (Structural networks)_500 Flocking behaviour_500 Agents







Traces of Agents.Image credit: http://www.generativeart.com/on/cic/2000/CARRANZA_COATES.HTM

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Evolutionary Swarm Systems- 3D Structural Networks

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1. Emergence seminar research by Sea Ahlquist and Moritz Fleishmann, 2008.

‘ The method of subdivision was easily derived from a method of 1D-scaling of a simple 3d polygonal shape. In this case a growth through simple itera-tive transform operation was desired, leading to highly articu-lated results of interconnected continuous geometries. ‘ [1]


// Precedent Study

Christina Doumpioti, MArch Dissertation, Architectural Associa-tion.2008Image credit: http://www.achimmenges.net/?p=4383

The point connections cofigurations definig the overal surface morphology. The points (agent) evaluate the stress peak of the system and establish the fibre path in referrence with the prin-cipal of stress.[2]

1. Christina Doumpioti, MArch Dissertation, Architectural Asso-ciation.20082. Michael Hensel & Michael Weinstock, Emergent Technologies and Design. Fibres .91. (Routledge : 2010)

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Christina Doumpioti, MArch Dissertation, Architectural Associa-tion.2008Image credit: http://www.achimmenges.net/?p=4383

The overall shape is derived through an iterative generation process. The surface nodes (agents) detectig the local stress peaks and determining the fibre path as the fiber structure of the shape. [2]

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Emergent Technologies and Design. Chapter:Fibres. 89

During the shape-generation process, points act as morphogen cells that self-organize and acquire their position and role within the system triggered by stress. During the fiber-generation process, new nodes extracted from the structure act as fibroblast cells that, trig-gered by stress concentrations, start to release fibers in the direction of the principal stresses.[1]

The process of computational growth operates in reference to the natural adaptive growth.Point or agents act as a morphogen cells, generating the overall shape. The iterative algorithmic procedure of the computational process creating a system that is self-organise and can forming a particular pattern of point distributions. The process will create the fundamental element of the shape with points act as intelligent agents which generated the fibre path (traces of agents) process. The points correspond with the stress concentrations, and generate fibres in the direction of principal stress. The points inde-pendently adjusting their position as to achieve a stress levelling across the entire system.[2]

The fibre path generation process includes the investigation of the stress type, directions and magnitude. The process starts with defin-ing a stress threshold value first with the nodes displaying the high-est stress concentrations. The nodes are defined as agents that orga-nise the fibre structure between the nodes. The agents organise the fibre paths (i.e. the fibre structure of the shape) in accordance to the principal stress. The vector of the principal stress provide the critical input for algorithmically generating the layout of the fibres.[2]

1. Christina Doumpioti, MArch Dissertation, Architectural Association.2008

2. Michael Hensel & Michael Weinstock, Emergent Technologies and Design. Fibres .91. (Routledge : 2010)

// Precedent Study

Computational Theory; Reflection

//Reductionism;Cited from the computational theory books, chapter 1 on introduction, it tells about how the universe is seen through the scientific point of view. It started with seeing the phenomena as reductionism, an approach of seeing thing in a form of multiples levels. In fact the hierarchy of science is organized I this man-ner as what referred by this chapter.

// Two types of phenomena, agents and interactions; To understand how the universe works, we can look can at two basic elements that operate in the universe system i.e. agent and interactions between them. This theory is suggested by the computational theory book in order to helps people understand the fundamental better. Agents for examples are molecules, ducks, cells and species. Examples of Interactions are the chemical reactions, immune system responses, duck mating and evolution. I would see this is im-portant in order to understand how the whole ideas work and helps to produce significance output deriving from the theory. An agent should not be analysed only itself as a case study. It is because it will limit the potential of expanding the knowledge which may arises from the analysis. The best way is to looking at both ways which are agents and their interactions.

// Readings


Description of images, will be updated later// snapshots from the video

// Three attributes agent interact with each other;

There are three attributes to understand and describe the interactions of agents. The attributes i.e. Collections, Iterations, and Adaptation, were further explained in the computational theory book. The book justified that Collections, Iterations and Adaptations is the universal concept as that they significantly important at-tributes for agents at all conditions. The books also explained in order to under-stand interactions, reader can begin with by forming set of questions. Those set of questions are i.e. what is X, what will X do, what happened when X meets Y, and what is Y. These set of questions may lead to answers explaining how the in-teractions work. The answer will exhibit the formation of equations, algorithmic and computational set of codes. Describing nature by analysing the interactions phenomena may leads to the discovery of various nature programs.

Collections, Multiplicity, Parallelism explaining the parallel collections of similar units will form emergent properties. For an example, ant colonies form by the sophistication of pattern formed by many ants. A parallel system has more advantages than the sequential system as the task can be performed continu-ously via specialization. From the understanding of the computational theory book, species in the world has various characteristics. Such characteristics are timid, strong, aggressive, passive and etc. All the characteristics of the species when combined altogether will form a unit, a system. The system encompasses of various species characteristics which lead to various possibilities of solutions and output. The strongest species will has the chances of survive and these phenomena described the natural selection process.

// Readings


Description of images, will be updated later// snapshots from the video


// NSS.AA

Natural System Studio_ Aiman Ahmad

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