En{code}D is an exhibition of 2009/2010 thesis projects from Master of Science in Adaptvie Architecture and Computation (Msc AAC). It’s on display at the Building Centre in London from October 4th to 8th. Drinks reception starts on the 5th at 6pm.

Atomized by Hugo Loureiro

The Cave by Ermis Adamantidis

Pixels Dance by Angelos Chronis

All of the works are available here.

If you are in London by the end of September void exhibit (AAC) is an event not to be missed. It’s the end of the year show of Msc Adaptive Architecture and Computation, starting at 6.30 pm on 25th of September. The venue is Arup’s “Phase 2 Exhibition Space”. Here is an excerpt from the exhibition description:

“The work combines computational models and physical pieces that address the themes of embedded, embodied and adaptive architecture. Each piece represents a scientific exploration of computation in the design process. There are analyses of space and structure, artificial intelligence techniques applied to design performance, and emergent systems built around bodily relationships between space and technology.”

Digital Hinterlands Exhibition will definitely be worth visiting as well. The exhibition, curated by Ruairi Glynn in collaboration with Arup, features works by recent graduates of the Bartlett, Architectural Association, Royal College of Arts and University of Westminster. Marilena Skavara’s “Adaptive Fa[ca]de” is this year’s Msc AAC thesis project which became part of this exhibition. You can find a description of this project below:

“Marilena Skavara’s ‘Adaptive Fa[ca]de’ explores the functional possibilities and performative characteristics of cellular automata (CA). In addition to the unique emergent behaviour of CA, a neural network enables a further computational layer to evolve CA behaviour to the context of its surrounding environment. Building upon the early work of Conway’s ‘Game of life’ and Stephen Wolfram’s extensive research on the wider implementation of CA, Skavara’s facade becomes a living adapting skin, constantly training itself from the history of its own errors and achievements.”

One more event which sounds very well is Digital Architecture London conference.

Discussions are divided into five thematic panels:

- space

- bio-technology

- interaction

- form

- fabrication

Alan Penn, Brett Steele and Hanif Kara are just of a few from many great academics and practitioners which will share their perspectives on computation and design. Take a look at the programme of the event for more details.

All three events are part of London Digital Week.

Msc AAC is a Master of Science open to practitioners and recent graduates in any design-related field. You can find an excerpt from the course description below:

“(…)we aim to provide students with a comprehensive understanding of the practical skills required to create generative, emergent and responsive form, through exposure to real programming environments, alongside a solid grounding in analytic and synthetic techniques and the creation of physical systems engendering interaction and adaptation.”

Full text is available on the course main site. I will be writing about Msc AAC students’ work during upcoming years. In the mean time you can take a look at some of the students’ work from this year: the programming part of the course is subdivided into two streams: morphogenetic programming and digital ecology.

image: “Dancer interacts with digital agents. A piece developed by Chiron Mottram (MSc AAC tutor 2005-2008)”

The video above is the final presentation of my second term programming project at the Msc Adaptive Architecture and Computation at the Bartlett, UCL. Underneath you can find a detailed description of the software as well as some photos of phenotypes fabricated for the 2nd term final exhibition which took place at Bartlett’s Wates House on Monday.

You can also take a look at previous posts about this project here and here, although I’m afraid that the explanations there will overlap with the one underneath.

external embryogeny

External embryogeny is a type of GA in which fitness function is pre-defined. In external embryogeny only the populations of solutions are subjects to evolution. It’s opposite is an implicit embryology GA – in which fitness objective evolves together with populations of solutions.

In the following month I’m going to write a paper about “breeding objects” software.
The research question which I hope to tackle in it is whether it is possible to harness an explicit embryology GA as a tool providing design solutions for both well-defined and ill-defined design problems. Natural and artificial selection is incorporated into the software in order to enable such possibility.

ill-defined and well-defined design problems

Well-defined design problem is a quantifiable problem. Budget constraint can be an example of such problem. Performance of the objects – for example the the amount of exposure to the light which they receive might be another example of it.

Ill-defined design problem in a problem which is not quantifiable. Aesthetics is a classic example. There has been many interesting attempts to research general patterns in aesthetic choices during design process, but, to date, every attempt to extract a general rule from the findings failed to some extend. The incorporation of natural and artificial selection into a GA might makes generalization obsolete.

phenotype generative procedure

The advantage of GA is that it can accommodate quite complex generative procedures. The final generative procedure is the 4th one which I was experimenting with. This proves that different procedures can be plugged to the same GA as long as their fitness objective is similar (this is explained in natural selection paragraph). The parameters of final procedure are the size of the object, resolution of the point grid as well as grid’s distortions caused by set of detractors and attractors. Finally range and strength of every attractor and detractor is also a parameter.

natural selection

Natural selection provides an answer to well-defined problems. In other words it is an optimization tool. In case of “breeding objects” software the problem (the fitness objective) is the cost of the object. Since the fabrication method is 2d laser cutting – the cost of the object consists of length of all of the cuts (which determines laser cutting time) as well as the area of the material.

During the natural selection step the software itself determines which individuals it should breed in order to create next generation. The choice is made based on the fitnesses of all of the individuals (the difference between their cost and the fitness objective).

The population quite quickly converges to a fit design solution. However if the process of natural selection continues the it is able to converge into another design solution. This solution is equally fit as the previous one (it’s cost is roughly the same) but it is very different visually.

artificial selection

The convergence from one fit design solution into another during natural selection proves that aesthetics is an ill-defined problem. This is where artificial selection comes in.

During the artificial selection step the user makes the choice about the individuals which become parents of next generation. User can choose just one individual or the whole population as the ones to breed.

The user is informed about the fitnesses of all of the individuals. Their choice can but does not have to be determined by this information. Additional information during this step is provided by 3 viewports enabled in the software: perspective, elevation and shading ratio (the amount of light which the surface underneath every individual receives).

natural & artificial

If the user will deliberately choose individuals with low fitness he or she is likely to loose the achievements of natural selection. This means that the next population can consist of less fit individuals than the previous one. It is not clear however, that it is always the case. What I’m actually going to investigate in the paper is whether that depends on some pre-determined parameters of both natural and artificial selection steps. Those parameters might for example be the percentage of mutation of each individual’s genotype or a range of fit individuals from which parents of next generation are chosen during natural selection.

Now, I have incorporated an artificial selection step into it. I took a look at what William Todd and Stephen Latham propose in “Evolutionary Design by Computers” (edited by Peter Bentley). They have suggested that, while simulating the process of evolution, there is no reason to limit the number of parents of future generations to only two. The software I’m writing already allows for a multiple choice of parents.

I have started working on a genetic algorithm which breeds object suitable for laser cutting. So far I’m only using artificial selection for the choice of most fit individuals. User is able to define the amount of time which the object will take to cut on a specific laser cutter. This value then serves as a fitness function (the closer particular phenotype is to desired cut time the more fit it is). Ultimately I’m planning to optimize for cost here. Generative procedure of the phenotype is what needs a lot of development too, but first I’m going to develop the entire GA setup.

Images above show that over time the whole population converges to a valid design solution. The breeding process, however, continues to look for even more suitable one. Thanks to that property software behavior becomes an illustration of the point which Steadman makes in Evolution of Designs – that quite different design solutions can be a valid answer to one well defined fitness condition. Aesthetics remain an ill defined problem, thus an artificial selection step where designer (or indeed a prosumer) can say which object he or she prefers to take forward is needed. This is why next step will be programming an artificial selection into the GA setup.

Most interestingly from designer’s point of view the same GA setup can work with entirely different generative procedures (entirely different phenotypes) if only the fitness objectives are the same. You can see my first try of the software underneath. I was dealing with a simple very simple phenotype generative procedure – stacking sections one on top of the other. What’s important is that It shows the same tendency as the more developed generative procedure – it converges to one design solution and then flips to a very different but still valid one.

Here is what Agata Guzik and me managed to script by the end of last day.

The easiest thing to do in GC is to populate a 2-dimensional grid of points (most probably “polygons by point grid” in GC nomenclature) with components. Sean introduced a way of breaking away from this notion by turning a 3-dimensional array of points into a 2-dimensional one. He still created a single plane of “polygons by point grid” but this time based on two 2d arrays of points arranged into one.

We then tried to rewrite the script so that it could incorporate more 2d arrays. We have created a new 3d array and we stacked 2d arrays (each containing two rows of points) one on top of another. Unfortunately it turned out that we can not use “polygons by point grid” any more – since it can be only fed with 2d array and not a 3d one. We have used a b spline surface by poles instead and then we have converted the surface into a set of polygons. Last step was (in order to celebrate GC’s name) population of the polygons with components which have a capacity of opening or closing (based on the value of graph variable).

This post follows the previous one on plugging Excel to Processing. As part of the Msc AAC I was presented with a task of visualizing data from scanning for blue-tooth devices in the city of Bath. I was working on this with Zeta Kachri, Ankon Mitra and Kensuke Hotta.

I’m skeptical about usefulness of the data from blue-tooth scanning as a mean of representation of any patterns of human presence in the city (since it turns out that only 8.5% of people have blue-tooth enabled in their devices). My group decided to try to explore the patterns of presence anyway. The generative procedure in Processing was driven by two parameters: number of scanned devices and (what we called) “persistence” of each device – the period for which it remained in the range of the scanner. We choose those parameters in order to represent places where people stay for longer period of time in opposite to places which majority of people just pass through. Next step might be an evaluation of the results of representation in the light of axial map analysis of the city of Bath coming from Space Syntax.

DXF exports from Processing needed a bit of tweaking with Rhinoscript before final export to STL.

The models where printed at the DMC London – a 3d printing centre located at the Bartlett School of Architecture. We have used a Z-Corp machine, but DMC offers a range of SLS machines from EOS too. I’m looking forward to using one of them next time.

We have chosen to print only the most representative data from the range of information provided by blue-tooth scan. You can see the whole set of data visualized in Processing underneath: