// NATURAL SYSTEM DESIGN STUDIO_ ALBUM

by AIMAN AHMAD (397733);

TASK 1.0 Lesson 01 and Lesson 02 Learning Processing Book

// Week 01 ;Step 1. Take your Lesson One design and rewrite it with variables instead of hard-coded values. Consider usinga for loop in the creation of your design.Step 2. Write a series of assignment operations that alter the values of those variables and make the designdynamic. You might also use system variables, such as width, height, mouseX , and mouseY .Step 3. Using conditional statements, alter the be-haviour of your design based on certain conditions. Whathappens if it touches the edge of the screen, or if it

grows to a certain size?

TASK 2.0 Organizations, Functions, Objects, Arrays.

// Week 02 ;

Step 1. Take the Class you made in Lesson Three and make an array of objects from that class.Step 2. Can you make the objects react to the mouse? Try using the dist( ) function to determine the object’sproximity to the mouse. For example, could you make each object jiggle more the closer it is to the mouse?How many objects can you make before the sketch runs

too slow?

TASK 3.0 Into Conclusion: Algorithims, Debugging, Libraries

// Week 03 ;

Step 1. Develop an idea for a project that can be cre-ated with Processing using simple shape drawing and thefundamentals of program-ming.Step 2. Follow the strategy outlined in this chapter and break the idea down into smaller parts, implementingthe algorithm for each one individually. Make sure to use object-oriented pro-gramming for each part.Step 3. Bring the smaller parts together in one

program.

TASK 4.0 Numbers and Vectors

// Week 04 ;Use the random walker as a template to simulate some real-world “natural” motion. Can you develop a set ofrules for simulating that behaviour? Ideas: nervous fly, hopping bunny, slithering snake, etc. (Consider thechallenge of using minimal visual design, i.e. b&w primi-tive shapes. Can you give your “being” a personality?Can it express emotions -- happiness, sadness, fear,

etc.?

SI_Swarm intelligenc is the collective behaviour of decentral-ized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.[1]SI systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of “intelligent” global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.The application of swarm principles to robots is called swarm robotics, while ‘swarm intelligence’ refers to the more general set of algorithms. ‘Swarm prediction’ has been used in the context of forecasting problems.

Ant colony optimization [ path to goals ]Ant colony optimization (ACO) is a class of optimization algo-rithms modeled on the actions of an ant colony. ACO methods are useful in problems that need to find paths to goals. Arti-ficial ‘ants’—simulation agents—locate optimal solutions by moving through a parameter space representing all possible solutions. Natural ants lay down pheromones directing each other to resources while exploring their environment. The simulated ‘ants’ similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions. www.wikipedia.org

// Small Project; Ants Nest

An analysis in the movement of individual ants in order to understand how the presence of pheromone affect their displacement and ultimately lead them to form routes con-necting their nest and various resources in the environment. Many ant species produce large dendritic networks of trails around their nest. These networks result from self-organized feedback mechanisms: ants leave small amounts of a chemi-cal -a pheromone- as they move across space. In turn, they are attracted by this same pheromone so that eventually a trail is formed.In both videos, researcher detected the presence of the ants and then colored the pixels covered by the ants with brighter colours as more ants passed over them. In this way, they can approximate the presence of the pheromone that ants of this particular species tend to lay on the ground almost all the time. The first video shows the process on a small portion of the experimental arena in real time while the second video shows the final result over the whole arena and a period of 60 minutes (here compressed in 15 seconds).Using this technique, they can characterise the ingredients of the feedback loop that ultimately leads to the formation of trails. The experiment show that the response to phero-mone concentrations is linear: an ant will turn to the left with frequency proportional to the difference between the pheromone concentrations on its left and right sides. Such a linear individual response was rejected by previous literature, as it would be incompatible with the results of a large number of experiments: trails can only be reinforced if the ants have a disproportionally higher probability to select the trail with higher pheromone concentration. However, the experiment show that the required non-linearity does not reside in the perceptual response of the ants, but in the noise associated with their movement. Courtesy from theswarmlab

// Analysis of behaviour ;

// snapshots from the video

PROJECT 1.0 ANTS NEST , TRAJECTORY _version aThe initial idea of this project is to create variations of spaces resulting from the movement of the points as a source of emergence. At this point, I am trying to produce a set of rule. I started with few simple rules first; - Creating two sets of points; small and big- Each point has its own data. Small points; act as a source of trajectory. Big points; creat-ing the character of the spaces.- Small points have been indicated with colours; red, green and blue- Big points exist in a form of circular hollow section ( the tunnel )The system;// Source (points) make a movement and produce the character of spaces

// Variation a;- Red ants initiate a move then follow by the bigger circle, thus create a tunnel.// Variation b;- Red ants make a linear move while creating the random tunnels// Variation c;- The tunnel character was produced by the movement of particular points.

The Potential Outcome;// establishing a system that can create a variations of spaces in relation to the prioritization of source (points). Thus, when it been applied to the architectural discourse, a set of space can be created in accordance to its hierarchical order with having their own set of rules. Furthermore, the series of spaces that have been created are not constrained with a predefined brief as what always happen in normal architectural discourse. A flexibility of outcome can be controlled within this system without limit them to a linear possibilities, but they can leads the outcome for multiple possibilities.

variation a

variation b

variation c

PROJECT 1.0 ANTS NEST , TRAJECTORY _version b

// Source (points) as a refference.

// The tunnel was created while follow the points// 3 variations of tunnels have been identified; > Near to right side of window; The tunnel compress > At center of the window; The tunnel expand > Random points; Moderate complexity of tunnels produced.

// Source (points) as a refference.

// compress // Expand // Moderate

// compress // Expand // Moderate

// compress // Expand // Moderate