Swarm modelling.

The use of Swarm Intelligence to generate architectural form.

'.neither the human purposes nor the architect's method are fully known in advance. Consequently, if this interpretation of the architectural problem situation is accepted, any problem-solving technique that relies on explicit problem definition, on distinct goal orientation, on data collection, or even on non-adaptive algorithms will distort the design process and the human purposes involved.' Stanford Anderson, "Problem-Solving and Problem-Worrying".

The works concentrates in the use of the computer as a perceptive device, a sort of virtual hand or "sense", capable of prompting an environment. From a set of data that conforms the environment (in this case the geometrical representation of the form of the site) this perceptive device is capable of differentiating and generating distinct patterns in its behavior, patterns that an observer has to interpret as meaningful information. As Nicholas Negroponte explains referring to the project GROPE in his Architecture Machine: 'In contrast to describing criteria and asking the machine to generate physical form, this exercise focuses on generating criteria from physical form.' 'The onlooking human or architecture machine observes what is "interesting" by observing GROPE's behavior rather than by receiving the testimony that this or that is "interesting".'

The swarm as a learning device.

In this case the work implements a Swarm as a perceptive device. Swarms constitute a paradigm of parallel systems: a multitude of simple individuals aggregate in colonies or groups, giving rise to collaborative behaviors. The individual sensors can't learn, but the swarm as a system can evolve in to more stable states. These states generate distinct patterns, a result of the inner mechanics of the swarm and of the particularities of the environment. The dynamics of the system allows it to learn and adapt to the environment; information is stored in the speed of the sensors (the more collisions, the slower) that acts as a memory. The speed increases in the absence of collisions and so providing the system with the ability to forget, indispensable for differentiation of information and emergence of patterns. The swarm is both a perceptive and a spatial phenomenon. For being able to Interact with an environment an observer requires some sort of embodiment. In the case of the swarm, its algorithms for moving, collision detection, and swarm mechanics conform its perceptive body. The way this body interacts with its environment in the process of learning and differentiation of spatial patterns constitutes also a spatial phenomenon.

The enactive space of the Swarm.

Enaction, a concept developed by Maturana and Varela for the description of perception in biological terms, is the understanding of perception as the result of the structural coupling of an environment and an observer. Enaction does not address cognition in the currently conventional sense as an internal manipulation of extrinsic 'information' or 'signals', but as the relation between environment and observer and the blurring of their identities. Thus, the space generated by the swarm is an enactive space, a space without explicit description, and an invention of the swarm-environment structural coupling. If we consider a gestalt as 'Some property -such as roundness- common to a set of sense data and appreciated by organisms or artefacts' (Gordon Pask), the swarm is also able to differentiate space 'gestalts' or spaces of some characteristics, such as 'narrowness', or 'fluidness' etc.

Implicit surfaces and the wrapping algorithm.

One of the many ways of describing this space is through the use of implicit surfaces. An implicit surface may be imagined as an infinitesimally thin band of some measurable quantity such as color, density, temperature, pressure, etc. Thus, an implicit surface consists of those points in three-space that satisfy some particular requirement. This allows as to wrap the regions of space where a difference of quantity has been produced, enclosing the spaces in which some particular events in the history of the Swarm have occurred. The wrapping method allows complex topologies, such as manifoldness in one continuous surface. It is possible to transform the information generated by the swarm in to a landscape that is the result of the particular reading of the site by the swarm.

Working in real time.

Because of the complex nature of the machine, the only possible way to evaluate the resulting behavior is in real time. For this purpose specific applications had to be developed, using OpenGL for the Windows programming environment. The package consisted on translators from DXF format to a specific format used by these applications and viceversa, the Swarm "engine", a simulated parallel environment, and the Wrapping programs, to generate the implicit surfaces. Different versions of each had been produced, in different stages of development of the work.