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Generative computation has the potential to enhance the accuracy, effectiveness, and creativity of spatial layout in design and planning. The paper proposes a methodology to separate the knowledge about objects, spatial relationships, and constraints from the generative process. The separation between the knowledge in a domain and its possible practical uses is an important achievement of semantic technologies, because it grants access to a large body of knowledge, spanning various aspects and processes across buildings and cities, which is being codified into formal ontologies. The present study has reused existing knowledge from two established ontologies. An illustrative case-project demonstrates the suitability of the methodology for a complex layout planning problem, involving a large number of decision-makers, with multiple competing objectives and criteria. The system implements multidimensional visual interactive tools to assist designers, planners, and decision-makers in exploring the layouts and the criteria, to develop their confidence in what qualifies as a good and effective solution.

The paper presents a methodology for describing in generative terms the structure of urban fabrics: the objective is to transfer conceptually the knowledge about the domain of urban space into a hierarchical and interrelated semantic structure with relevant concepts, elements and their mutual relationships, providing explicit and unambiguous definitions. The conceptual and operational instrument adopted for this purpose is the ontology, a method of knowledge representation and management coming from the Artificial Intelligence. This approach aims to create a customisable digital design tool, to support the designer in the early stages of urban design process, such as street pattern and massing definition, by generating in real time a number of design scenarios, starting from a large number of constraints and requests. This paper focuses on the knowledge formalisation aspects of the research that is the basis for the generative modelling of urban space.

This paper introduces a dynamic generative modelling system for urban and regional design. Through dynamic modelling the system evolves in time according to the interactions of the planners, decision-makers and citizens. On the basis of several synchronous and/or asynchronous user interactions, models are dynamically generated at run time. The models are built by defining the data (datasets) and the actions to perform on that data (tasks). The system reads and correlates data at urban and regional scale from various authorities to generate dynamic datasets. Tasks are especially powerful when they integrate generative procedures in a hierarchical structure. This allows us to model urban and regional dynamics through the interaction of tasks at micro- and macro-scale. Tasks can also implement either Cellular Automata or software agents. We examine the system application to a case project: the simulation of micro- and macro-dynamics in an Alpine valley, with specific challenges to fit competitive and sustainable growth in a landscape quality perspective. The simulation in spatial and temporal dimensions of regional data provided us with the elements to study the territorial evolution over the next twenty years. Four strategies gave as many scenarios highlighting the results of specific policies.