TY  - CHAP
A1  - König, Reinhard
A1  - Bauriedel, Christian
T1  - Computer-generated Urban Structures
T2  - Proceedings of the Generative Art Conference
N2  - How does it come to particular structure formations in the cities and which strengths play a role in this process? On which elements can the phenomena be reduced to find the respective combination rules? How do general principles have to be formulated to be able to describe the urban processes so that different structural qualities can be produced? With the aid of mathematic methods, models based on four basic levels are generated in the computer, through which the connections between the elements and the rules of their interaction can be examined. Conclusions on the function of developing processes and the further urban origin can be derived.
KW  - Computational Urban Design
Y1  - 2004
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20160623-26090
SP  - 1
EP  - 10
CY  - Milan, Italy
ER  - 
TY  - CHAP
A1  - König, Reinhard
A1  - Schmitt, Gerhard
ED  - Szoboszlai, Mihály
T1  - Backcasting and a new way of command in computational design : Proceedings
T2  - CAADence in Architecture Conference
N2  - It's not uncommon that analysis and simulation methods are used mainly to evaluate finished designs and to proof their quality. Whereas the potential of such methods is to lead or control a design process from the beginning on. Therefore, we introduce a design method that move away from a “what-if” forecasting philosophy and increase the focus on backcasting approaches. We use the power of computation by combining sophisticated methods to generate design with analysis methods to close the gap between analysis and synthesis of designs. For the development of a future-oriented computational design support we need to be aware of the human designer’s role. A productive combination of the excellence of human cognition with the power of modern computing technology is needed. We call this approach “cognitive design computing”. The computational part aim to mimic the way a designer’s brain works by combining state-of-the-art optimization and machine learning approaches with available simulation methods. The cognition part respects the complex nature of design problems by the provision of models for human-computation interaction. This means that a design problem is distributed between computer and designer. In the context of the conference slogan “back to command”, we ask how we may imagine the command over a cognitive design computing system. We expect that designers will need to let go control of some parts of the design process to machines, but in exchange they will get a new powerful command on complex computing processes. This means that designers have to explore the potentials of their role as commanders of partially automated design processes. In this contribution we describe an approach for the development of a future cognitive design computing system with the focus on urban design issues. The aim of this system is to enable an urban planner to treat a planning problem as a backcasting problem by defining what performance a design solution should achieve and to automatically query or generate a set of best possible solutions. This kind of computational planning process offers proof that the designer meets the original explicitly defined design requirements. A key way in which digital tools can support designers is by generating design proposals. Evolutionary multi-criteria optimization methods allow us to explore a multi-dimensional design space and provide a basis for the designer to evaluate contradicting requirements: a task urban planners are faced with frequently. We also reflect why designers will give more and more control to machines. Therefore, we investigate first approaches learn how designers use computational design support systems in combination with manual design strategies to deal with urban design problems by employing machine learning methods. By observing how designers work, it is possible to derive more complex artificial solution strategies that can help computers make better suggestions in the future.
KW  - Cognitive design computing
KW  - machine learning
KW  - backcasting
KW  - design synthesis
KW  - evolutionary optimization
KW  - CAD
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20160622-25996
SP  - 15
EP  - 25
CY  - Budapest
ER  -