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 - TY - CHAP A1 - König, Reinhard A1 - Treyer, Lukas A1 - Schmitt, Gerhard T1 - Graphical smalltalk with my optimization system for urban planning tasks T2 - 31st eCAADe Conference – Volume 2 N2 - Based on the description of a conceptual framework for the representation of planning problems on various scales, we introduce an evolutionary design optimization system. This system is exemplified by means of the generation of street networks with locally defined properties for centrality. We show three different scenarios for planning requirements and evaluate the resulting structures with respect to the requirements of our framework. Finally the potentials and challenges of the presented approach are discussed in detail. KW - Städtebau KW - Architektur KW - Design optimization KW - evolutionary multi-criteria optimization KW - generative system integration KW - interactive planning support system Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20160121-25171 SP - 195 EP - 203 PB - TU Delft CY - Delft, Netherlands ER - TY - CHAP A1 - König, Reinhard A1 - Schneider, Sven A1 - Hijazi, Ihab Hamzi A1 - Li, Xin A1 - Bielik, Martin A1 - Schmitt, Gerhard A1 - Donath, Dirk T1 - Using geo statistical analysis to detect similarities in emotional responses of urban walkers to urban space T2 - Sixth International Conference on Design Computing and Cognition (DCC14) N2 - Using geo statistical analysis to detect similarities in emotional responses of urban walkers to urban space KW - Städtebau Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20160121-25146 ER -