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CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
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_id	ga0007
id	ga0007
authors	Coates, Paul and Miranda, Pablo
year	2000
title	Swarm modelling. The use of Swarm Intelligence to generate architectural form
source	International Conference on Generative Art
summary	.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.
series	other
email
more	http://www.generativeart.com/
last changed	2003/08/07 17:25

_id	60e7
authors	Bailey, Rohan
year	2000
title	The Intelligent Sketch: Developing a Conceptual Model for a Digital Design Assistant
doi	https://doi.org/10.52842/conf.acadia.2000.137
source	Eternity, Infinity and Virtuality in Architecture [Proceedings of the 22nd Annual Conference of the Association for Computer-Aided Design in Architecture / 1-880250-09-8] Washington D.C. 19-22 October 2000, pp. 137-145
summary	The computer is a relatively new tool in the practice of Architecture. Since its introduction, there has been a desire amongst designers to use this new tool quite early in the design process. However, contrary to this desire, most Architects today use pen and paper in the very early stages of design to sketch. Architects solve problems by thinking visually. One of the most important tools that the Architect has at his disposal in the design process is the hand sketch. This iterative way of testing ideas and informing the design process with images fundamentally directs and aids the architect’s decision making. It has been said (Schön and Wiggins 1992) that sketching is about the reflective conversation designers have with images and ideas conveyed by the act of drawing. It is highly dependent on feedback. This “conversation” is an area worthy of investigation. Understanding this “conversation” is significant to understanding how we might apply the computer to enhance the designer’s ability to capture, manipulate and reflect on ideas during conceptual design. This paper discusses sketching and its relation to design thinking. It explores the conversations that designers engage in with the media they use. This is done through the explanation of a protocol analysis method. Protocol analysis used in the field of psychology, has been used extensively by Eastman et al (starting in the early 70s) as a method to elicit information about design thinking. In the pilot experiment described in this paper, two persons are used. One plays the role of the “hand” while the other is the “mind”- the two elements that are involved in the design “conversation”. This variation on classical protocol analysis sets out to discover how “intelligent” the hand should be to enhance design by reflection. The paper describes the procedures entailed in the pilot experiment and the resulting data. The paper then concludes by discussing future intentions for research and the far reaching possibilities for use of the computer in architectural studio teaching (as teaching aids) as well as a digital design assistant in conceptual design.
keywords	CAAD, Sketching, Protocol Analysis, Design Thinking, Design Education
series	ACADIA
last changed	2022/06/07 07:54

_id	1bb0
authors	Russell, S. and Norvig, P.
year	1995
title	Artificial Intelligence: A Modern Approach
source	Prentice Hall, Englewood Cliffs, NJ
summary	Humankind has given itself the scientific name homo sapiens--man the wise--because our mental capacities are so important to our everyday lives and our sense of self. The field of artificial intelligence, or AI, attempts to understand intelligent entities. Thus, one reason to study it is to learn more about ourselves. But unlike philosophy and psychology, which are also concerned with AI strives to build intelligent entities as well as understand them. Another reason to study AI is that these constructed intelligent entities are interesting and useful in their own right. AI has produced many significant and impressive products even at this early stage in its development. Although no one can predict the future in detail, it is clear that computers with human-level intelligence (or better) would have a huge impact on our everyday lives and on the future course of civilization. AI addresses one of the ultimate puzzles. How is it possible for a slow, tiny brain{brain}, whether biological or electronic, to perceive, understand, predict, and manipulate a world far larger and more complicated than itself? How do we go about making something with those properties? These are hard questions, but unlike the search for faster-than-light travel or an antigravity device, the researcher in AI has solid evidence that the quest is possible. All the researcher has to do is look in the mirror to see an example of an intelligent system. AI is one of the newest disciplines. It was formally initiated in 1956, when the name was coined, although at that point work had been under way for about five years. Along with modern genetics, it is regularly cited as the ``field I would most like to be in'' by scientists in other disciplines. A student in physics might reasonably feel that all the good ideas have already been taken by Galileo, Newton, Einstein, and the rest, and that it takes many years of study before one can contribute new ideas. AI, on the other hand, still has openings for a full-time Einstein. The study of intelligence is also one of the oldest disciplines. For over 2000 years, philosophers have tried to understand how seeing, learning, remembering, and reasoning could, or should, be done. The advent of usable computers in the early 1950s turned the learned but armchair speculation concerning these mental faculties into a real experimental and theoretical discipline. Many felt that the new ``Electronic Super-Brains'' had unlimited potential for intelligence. ``Faster Than Einstein'' was a typical headline. But as well as providing a vehicle for creating artificially intelligent entities, the computer provides a tool for testing theories of intelligence, and many theories failed to withstand the test--a case of ``out of the armchair, into the fire.'' AI has turned out to be more difficult than many at first imagined, and modern ideas are much richer, more subtle, and more interesting as a result. AI currently encompasses a huge variety of subfields, from general-purpose areas such as perception and logical reasoning, to specific tasks such as playing chess, proving mathematical theorems, writing poetry{poetry}, and diagnosing diseases. Often, scientists in other fields move gradually into artificial intelligence, where they find the tools and vocabulary to systematize and automate the intellectual tasks on which they have been working all their lives. Similarly, workers in AI can choose to apply their methods to any area of human intellectual endeavor. In this sense, it is truly a universal field.
series	other
last changed	2003/04/23 15:14

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