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Our approach to Generative Design converts the problems of design from the geometrical drawing of shapes in a continuous setting to topological decision making about spatial configurations in a discrete setting. The paper presents a comprehensive formulation of the zoning problem as a sub-problem of architectural 3D layout configurations. This form...
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... Therefore discretization is the process of breaking down the integrated design problem into multiple smaller yet interdependent decision problems. An example of such discretization can be a voxel grid that provides a non-biased and homogeneous representation of spatial units, each of which poses a decision problem of function allocation ) & (Soman, Aditya, Azadi, Shervin, and Nourian, Pirouz 2022). ...
... Possibilistic Approach The essence of the possibilistic approach to design is using a multi-valued or non-binary logic framework for making design decisions, typically in the sense of making discrete choices about discrete segments of space, for example, the Markovian Design Machines of (Batty 1974), the Spatial Agents Academy of (Veloso and Krishnamurti 2020), and MAGMA (Multi-Attribute Gradient-Driven Mass Aggregation) through Fuzzy Logic as introduced briefly in Nourian (2016) and Soman, Aditya, Azadi, Shervin, and Nourian, Pirouz (2022). Both of these methodologies apply non-binary logic from a possibilistic point of view, in the sense that they take design inputs that are valued in the range of [0, 1] but treat them as possibility measures rather than probability measures. ...
... Therefore discretization is the process of breaking down the integrated design problem into multiple smaller yet interdependent decision problems. An example of such discretization can be a voxel grid that provides a non-biased and homogeneous representation of spatial units, each of which poses a decision problem of function allocation ) & (Soman, Aditya, Azadi, Shervin, and Nourian, Pirouz 2022). ...
... Possibilistic Approach The essence of the possibilistic approach to design is using a multi-valued or non-binary logic framework for making design decisions, typically in the sense of making discrete choices about discrete segments of space, for example, the Markovian Design Machines of (Batty 1974), the Spatial Agents Academy of (Veloso and Krishnamurti 2020), and MAGMA (Multi-Attribute Gradient-Driven Mass Aggregation) through Fuzzy Logic as introduced briefly in Nourian (2016) and Soman, Aditya, Azadi, Shervin, and Nourian, Pirouz (2022). Both of these methodologies apply non-binary logic from a possibilistic point of view, in the sense that they take design inputs that are valued in the range of [0, 1] but treat them as possibility measures rather than probability measures. ...
This chapter presents methodological reflections on the necessity and utility of artificial intelligence in generative design. Specifically, the chapter discusses how generative design processes can be augmented by AI to deliver in terms of a few outcomes of interest or performance indicators while dealing with hundreds or thousands of small decisions. The core of the performance-based generative design paradigm is about making statistical or simulation-driven associations between these choices and consequences for mapping and navigating such a complex decision space. This chapter will discuss promising directions in Artificial Intelligence for augmenting decision-making processes in architectural design for mapping and navigating complex design spaces.
... A recent example is using artificial agents for exhausting and mapping all possible scenarios in a serious game environment to achieve a holistic picture of the design space [145]. This approach has also been applied for design configurations in the GoDesign framework [26] where the authors propose using ABM to allocate the program of requirement in the predefined envelope (see Figure 12 [154].) Similar to the ABM, artificial agents can be defined in the simulation environment to explore the design space and learn about dynamics of it under the Reinforcemont Learning framework, for example for configuring architectural spaces [155]. ...
... Thus, instead of focusing on the generative capabilities of generative models for generating realistic pictures in the styles learnt from a corpus of humans (such as the 2D or 3D images typically generated with Generative Adversarial Networks or DALL.E). Regardless of whether it is a utopian or dystopian future for AI models to generate architec- [154], [164] tural designs, the technological possibility for generating a design is already available. The more critical question of interest regards the capability of AI for solving hard problems of performance-based generative design [165] where mapping or navigating the design space is intractable due to the difficulty of formulating the associations of choices and consequences, see a comprehensive review of the applications of deep generative models in design [166]. ...
This chapter provides a methodological overview of generative design in architecture, especially highlighting the commonalities between three separate lineages of generative approaches in architectural design, namely the mathematical optimization methods for topology optimization and shape optimization, generative grammars (shape grammars and graph grammars), and [agent-based] design games. A comprehensive definition of generative design is provided as an umbrella term referring to the mathematical, grammatical, or gamified methodologies for systematic synthesis, i.e. derivation, itemization, or exploration of configurations. Among other points, it is shown that generative design methods are not necessarily meant to automate design but rather provide structured mechanisms to facilitate participatory design or creative mass customization. Effectively, the chapter provides the theoretical minimum for understanding generative design as a paradigm in computational design; demystifies the term generative design as a technological hype; shows a precis of the history of the generative approaches in architectural design; provides a minimalist methodological framework summarising lessons from the three lineages of generative design; and deepens the technological discourse on generative design methods by reflecting on the topological constructs and techniques required for devising generative systems or design machines, including those equipped with Artificial Intelligence. Moreover, the notions of discrete design and design for discrete assembly are discussed as precursors to the core concept of design as decision-making in generative design, thus hinting to avenues of future research in manufacturing-informed combinatorial mass customization and discrete architecture in tandem with generative design methods.
... This work continues a dialog with contemporary research in the housing field first with the problem of mass customizing houses for the different needs of the assisted families, [6][7][8][9] and second with the problem of participatory design in architecture. [10][11][12] Data collection ...
In Portugal, in the 1960s and 1970s, there was research concerning a system of the architectural design of housing for economically less favored populations, which related sociological information with analogical computational methods and culminated with its application in the Local Ambulatory Support Service (SAAL). This article presents the digitization process of these methods for the development of an architectural design system for social housing. The main goal is to improve methodological procedures for the original research and, specifically, to adapt them to computational design and modeling processes. To this end, this research transposed the aforementioned methodology into an algorithmic model that matches sociological information acquired from an online form with a database of social housing floor plan images to generate a building information modeling (BIM) directly from the selected image source. The result is an algorithmic model informed by sociological data linked with a BIM model to enable further rationalization of architectural design.
