TY - CHAP A1 - König, Reinhard ED - Martens, Bob ED - Wurzer, G, Gabriel ED - Grasl, Tomas ED - Lorenz, Wolfgang ED - Schaffranek, Richard T1 - CPlan: An Open Source Library for Computational Analysis and Synthesis T2 - 33rd eCAADe Conference N2 - Some caad packages offer additional support for the optimization of spatial configurations, but the possibilities for applying optimization are usually limited either by the complexity of the data model or by the constraints of the underlying caad system. Since we missed a system that allows to experiment with optimization techniques for the synthesis of spatial configurations, we developed a collection of methods over the past years. This collection is now combined in the presented open source library for computational planning synthesis, called CPlan. The aim of the library is to provide an easy to use programming framework with a flat learning curve for people with basic programming knowledge. It offers an extensible structure that allows to add new customized parts for various purposes. In this paper the existing functionality of the CPlan library is described. KW - Architektur KW - Computer KW - CAAD KW - cplan KW - CAD Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20160118-25037 SP - 245 EP - 250 PB - Vienna University of Technology CY - Vienna ER - TY - JOUR A1 - König, Reinhard T1 - Computers in the design phase - Ten thesis on their uselessness T1 - Der Computer in der Entwurfsphase - Zehn Thesen zu seiner Nutzlosigkeit JF - Der Generalist N2 - At the end of the 1960s, architects at various universities world- wide began to explore the potential of computer technology for their profession. With the decline in prices for PCs in the 1990s and the development of various computer-aided architectural design systems (CAAD), the use of such systems in architectural and planning offices grew continuously. Because today no ar- chitectural office manages without a costly CAAD system and because intensive soſtware training has become an integral part of a university education, the question arises about what influence the various computer systems have had on the design process forming the core of architectural practice. The text at hand devel- ops ten theses about why there has been no success to this day in introducing computers such that new qualitative possibilities for design result. RESTRICTEDNESS KW - computational design KW - CAD Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20160622-26075 ER - TY - JOUR A1 - König, Reinhard T1 - Die Stadt der Agenten und Automaten JF - FORUM - Architektur & Bauforum N2 - PLANUNGSUNTERSTÜTZUNG DURCH DIE ANALYSE RÄUMLICHER PROZESSE MITTELS COMPUTERSIMULATIONEN. Erst wenn man – zumindest im Prinzip – versteht, wie eine Stadt mit ihren komplexen, verwobenen Vorgängen im Wesentlichen funktioniert, ist eine sinnvolle Stadtplanung möglich. Denn jede Planung bedeutet einen Eingriff in den komplexen Organismus einer Stadt. Findet dieser Eingriff ohne Wissen über die Funktionsweise des Organismus statt, können auch die Auswirkungen nicht abgeschätzt werden. Dieser Beitrag stellt dar, wie urbane Prozesse mittels Computersimulationen unter Zuhilfenahme so genannter Multi-Agenten-Systeme und Zellulärer Automaten verstanden werden können. von KW - Computational Urban Design KW - CAD Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20160622-26083 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 -