Posts Tagged ‘Cellular Automata’
Animus
Interactive Installation @ MAT End of the Year Show 2011. (June 9, 2011)
Animus is an interactive multimedia installation inspired by Quorum Sensing in bacteria. It stands as a metaphor for the governing spirit of the system, a collective brain if you will, for cell to cell communication in microbes which in turn defines its gene expression and the collective behaviors that emerge from it. Micro organisms use this kind of communication constantly to check for their population density and crossing a certain threshold display behaviors that vary from bioluminescence and toxic secretion to sporulation and conjugation.
Animus places the user as a controller for this system, where how they choose to interact with it defines its outcomes. The user gets a continuous feedback of the threshold required to display a certain behavior (bioluminescence in this case), affecting how they interact with it, making them more a part of the system then its controller eventually.
Installation Description
The installation uses a Kinect to create a sensing field in front of the projection, making direct manipulation of the system possible unaffected by any background noise. The user needs to perform a certain gesture to identify them as the controller and start affecting the system’s behavior reflected by the audio interface and the display projection.
Morphon
3D (Stereographic) Visualization based on Cellular Automata
(Part of the MAT End of Year Show 2011, in Collaboration with Qian Liu)
Morphon is a visualization of Cellular Automata in 3D, for a specific rule set and the structures/patterns that emerge from it. Cellular Automaton is a discrete mathematical model studied in a number of advanced scientific fields. In its most elementary form it is a one dimensional row of cells, each with one of two possible states (ON or OFF) at any discrete moment of time, based on the states of its two neighboring cells and its previous self. There are rules governing this change of states over time, and thus emerges a complex mix of patterns sometimes periodic and sometimes highly stochastic from very basic building blocks.
This project investigates a subset of an extremely wide range of possibilities to visualize 3D Cellular Automata and explore the generative structures that emerge in the process. The user can interact with the emergent structures with an iPhone interface that communicates with the app using OSC messages. The sonification process is very basic in that it maps the number of living cells at any time to the number of instruments.
Visualizing Cellular Automata – III
( …Continued from Visualizing Cellular Automata – II )
The possibilities to explore these structures were immense and so I skipped the next logical part where normally I would simulate 2D Cell Automata (a more specific example of which is “Conway’s Game of Life”) with 4294967296 (2^(2^5)) possibilities to explore. Instead I moved on to explore 3D CA. With millions of possibilities to explore again, it becomes crucial to constraint the system. So I explored Totalistic CA, where the rules are not updated based on state of a single cell, but based on the sum of their states, in a cell’s neighborhood. I ended up getting some interesting structures as shown in the images below. The sonification for this is rather primitive with the number of Cells being updated directly affecting the frequencies.
Visualizing Cellular Automata – II
( …Continued from Visualizing Cellular Automata – I )
Since the system has many cells, and hence many states it was important to constraint it somehow when attempting to sonify it. Some attempts at creating generative music are following. The last video is an example of mapping Rule 110 on Pelog Scale to generate rythmic sounds, midway through which I dynamically increment/decrement the rules to change the structure.
Cellular Automata on Pelog Scale from Ritesh L on Vimeo.
( Continued on Visualizing Cellular Automata – III… )
Visualizing Cellular Automata – I
Genetics has been a topic I was interested in for as long as I can remember. Recently I started reading about how biological systems function and how their principles are applied in creating programs. This led me to read more about genetic programming and AI, and eventually I came across Complex Systems and Cellular Automata. A quick google search was enough to get me excited about its generative nature and emergent patterns. The fact that I could create a purely rational system on my computer which applied logical rules on a set of states to generate such complex structures- orderly and chaotic at the same time, encouraged me to explore this as a project for 594P.
The origins of Cell Automaton lie in Von Neumann’s simplification of the process of Kinematic Automata, a system designed to create self-replicating robots, due to Stanislaw Ulam’s insight on his methods. Though it became popular within a small computing community with John Conway’s “Game of Life”, it was Stephen Wolfram’s publication of “A New Kind of Science”, a book that explains how complex systems emerge from seemingly simplistic ones like Cell Automata, that reintroduced its concept as a thoroughly systematic investigation. The basics are very straightforward- you start with a set of initial states, iterate through all the cells, checking each cell’s neighborhood (a finite number of cells around it) and mapping its states to the rule being employed to calculate the next state of the cell. All the cells are updated once the rule is employed and then the process is repeated.
I started with Elementary Cellular Automata- 1D structure of cells, where each cell’s neighborhood is composed of itself, the cell on its right and the cell on its left, and there are only two possible states for each cell: ’0′ and ’1′. With this configuration you have a possibility of 256 (2^(2^3)) rules to govern the behavior. Interesting behaviors emerge when the evolution of 1D cellular automata is tracked for a number of iterations. The following images display some of the interesting rules. The major observation Wolfram made was how some structures were very orderly while some very stochastic in nature. Although some of the most interesting ones are with a combination of both, order and randomness in their structure, for example rule 110.
( Continued on Visualizing Cellular Automata – II… )