2011 - 2020


Multi Agent Systems, Complexity Research, Generative Design, Robotics

Description: My research lies on the intersection of computational design, performance based engineering, and robotic fabrication with the objective to facilitate design exploration in the early design stage and reduce design complexity. Design can be described as the process of emergence and discovery resulting from the description of the design problem, the definition of the design parameters and their relationship with the constraints. These constraints stem from the available resources and can be of either material, environmental or socio-economic nature. While these may initially prove to be a limitation, over the course of the design process they can evolve into a driver for innovative design solutions. My work seeks to deliver novel approaches and computational methods and tools that will extend the designer’s ability to handle complex design problems. Form finding, environmental analysis, material properties and robotic construction limitations can be accounted by bringing available algorithmic tools into play and by using performance data to evaluate designs. The overall aim is to come up with formal methods capable of encompassing a number of performance criteria and objectives into the design and fabrication process.

In my Ph.D. research, I defined an innovative approach for extending form finding techniques by incorporating behaviors using a Multi Agent Systems (MAS) approach with the objective of considering environmental and structural parameters as design drivers early in the design stage. By employing agent based modelling and stochastic methods along with performance simulations I was able to develop a MAS framework that allows designers to generate, analyze and evaluate a large number of design alternatives. This is achieved through developing a custom toolkit which bridges existing 3d modelling software with analytical solvers and extends their functionality. My work seeks to employ computational analysis and optimization algorithms as an integral source for generating and evaluating design alternatives and extending designers’ intuition by visualizing performance related data.