Die Stadt der Agenten und Automaten

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. vo

Der Computer in der Entwurfsphase - Zehn Thesen zu seiner Nutzlosigkeit

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. RESTRICTEDNES

CPlan: An Open Source Library for Computational Analysis and Synthesis

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

Computational Urban Planning: Using the Value Lab as Control Center

Urban planning involves many aspects and various disciplines, demanding an asynchronous planning approach. The level of complexity rises with each aspect to be considered and makes it difficult to find universally satisfactory solutions. To improve this situation we propose a new approach, which complement traditional design methods with a computational urban plan- ning method that can fulfil formalizable design requirements automatically. Based on this approach we present a design space exploration framework for complex urban planning projects. For a better understanding of the idea of design space exploration, we introduce the concept of a digital scout which guides planners through the design space and assists them in their creative explorations. The scout can support planners during manual design by informing them about potential im- pacts or by suggesting different solutions that fulfill predefined quality requirements. The planner can change flexibly between a manually controlled and a completely automated design process. The developed system is presented using an exemplary urban planning scenario on two levels from the street layout to the placement of building volumes. Based on Self-Organizing Maps we implemented a method which makes it possible to visualize the multi-dimensional solution space in an easily analysable and comprehensible form

Backcasting and a new way of command in computational design : Proceedings

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

Spatial Optimizations: Merging depthmapX , spatial graph networks and evolutionary design in Grasshopper

In the Space Syntax community, the standard tool for computing all kinds of spatial graph network measures is depthmapX (Turner, 2004; Varoudis, 2012). The process of evaluating many design variants of networks is relatively complicated, since they need to be drawn in a separated CAD system, exported and imported in depthmapX via dxf file format. This procedure disables a continuous integration into a design process. Furthermore, the standalone character of depthmapX makes it impossible to use its network centrality calculation for optimization processes. To overcome this limitations, we present in this paper the first steps of experimenting with a Grasshopper component (reference omitted until final version) that can access the functions of depthmapX and integrate them into Grasshopper/Rhino3D. Here the component is implemented in a way that it can be used directly for an evolutionary algorithm (EA) implemented in a Python scripting component in Grasshoppe

Graphical smalltalk with my optimization system for urban planning tasks

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

Lightweight urban computation interchange (LUCI) system

In this paper we introduce LUCI, a Lightweight Urban Calculation Interchange system, designed to bring the advantages of a calculation and content co-ordination system to small planning and design groups by the means of an open source middle-ware. The middle-ware focuses on problems typical to urban planning and therefore features a geo-data repository as well as a job runtime administration, to coordinate simulation models and its multiple views. The described system architecture is accompanied by two exemplary use cases that have been used to test and further develop our concepts and implementations

Evolutionäre Generierung von Grundriss-Layouts mithilfe von Unterteilungsalgorithmen

Das Unterteilen einer vorgegebenen Grundfläche in Zonen und Räume ist eine im Architekturentwurf häufig eingesetzte Methode zur Grundrissentwicklung. Für deren Automatisierung können Unterteilungsalgorithmen betrachtet werden, die einen vorgegebenen, mehrdimensionalen Raum nach einer festgelegten Regel unterteilen. Neben dem Einsatz in der Computergrafik zur Polygondarstellung und im Floorplanning zur Optimierung von Platinen-, Chip- und Anlagenlayouts finden Unterteilungsalgorithmen zunehmend Anwendung bei der automatischen Generierung von Stadt- und Gebäudegrundrissen, insbesondere in Computerspielen. Im Rahmen des Forschungsprojekts Kremlas wurde das gestalterische und generative Potential von Unterteilungsalgorithmen im Hinblick auf architektonische Fragestellungen und ihre Einsatzmöglichkeiten zur Entwicklung einer kreativen evolutionären Entwurfsmethode zur Lösung von Layoutproblemen in Architektur und Städtebau untersucht. Es entstand ein generativer Mechanismus, der eine Unterteilungsfolge zufällig erstellt und Grundrisse mit einer festgelegten Anzahl an Räumen mit bestimmter Raumgröße durch Unterteilung generiert. In Kombination mit evolutionären Algorithmen lassen sich die erhaltenen Layoutlösungen zudem hinsichtlich architektonisch relevanter Kriterien optimieren, für die im vorliegenden Fall Nachbarschaftsbeziehungen zwischen einzelnen Räumen betrachtet wurden

KREMLAS: Entwicklung einer kreativen evolutionären Entwurfsmethode für Layoutprobleme in Architektur und Städtebau

Die im vorliegenden Buch dokumentierten Untersuchungen befassen sich mit der Entwicklung von Methoden zur algorithmischen Lösung von Layoutaufgaben im architektonischen Kontext. Layout bezeichnet hier die gestalterisch und funktional sinnvolle Anordnung räumlicher Elemente, z.B. von Parzellen, Gebäuden, Räumen auf bestimmten Maßstabsebenen. Die vorliegenden Untersuchungen sind im Rahmen eines von der Deutschen Forschungsgemeinschaft geförderten Forschungsprojekts entstanden