Design agents and the need for high-dimensional perception

Designed artefacts may be quantified by any number of measures. This paper aims to show that in doing so, the particular measures used may matter very little, but as many as possible should be taken. A set of building plans is used to demonstrate that arbitrary measures of their shape serve to classify them into neighbourhood types, and the accuracy of classification increases as more are used, even if the dimensionality of the space in which classification occurs is held constant. It is further shown that two autonomous agents may independently choose sets of attributes by which to represent the buildings, but arrive at similar judgements as more are used. This has several implications for studying or simulating design. It suggests that quantitative studies of collections of artefacts may be made without requiring extensive knowledge of the best possible measures—often impossible in real, ill-defined, design situations. It suggests a means by which the generation of novelty can be explained in a group of agents with different ways of seeing a given event. It also suggests that communication can occur without the need for predetermined codes or protocols, introducing the possibility of alternative human-computer interfaces that may be useful in design

Structural Evolution: a genetic algorithm method to generate structurally optimal Delaunay triangulated space frames for dynamic loads

An important principle in the architectural design process is the quest for the optimum solution, a quest which is in this study structurally motivated and necessarily computationally oriented given its high complexity in nature. The present research project suggests an evolutionary algorithm that draws its power from the literal interpretation of the natural system's reproductive process at a microscopic scale with the scope of generating optimal Delaunay triangulated space frames for dynamic loads. The algorithm repositions a firm number of nodes within a space envelope, by establishing Delaunay tetrahedra and consequently creating adaptable optimised space frame topologies. The arbitrarily generated tetrahedralised structure is compared against a canonical designed one, whilst several experiments are conducted in order to investigate whether -and to what degree- the genetic algorithm method is appropriate for searching discontinuous and difficult solution spaces or not. The results of this comparison indicate that the method proposed has advantageous properties while being capable of generating an optimum structure that exceeds statically the performance of an engineered tetrahedralised space frame

Computer vision and optimization methods applied to the measurements of in-plane deformations

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Topological Self-Organisation: Using a particle-spring system simulation to generate structural space-filling lattices

The problem being addressed relates to the filling of a certain volume with a structural space frame network lattice consisting of a given number of nodes. A method is proposed that comprises a generative algorithm including a physical dynamic simulation of particle-spring system. The algorithm is able to arrange nodes in space and establish connections among them through local rules of self-organisation, thus producing space frame topologies. In order to determine the appropriateness of the method, an experiment is conducted that involves testing the algorithm in the case of filling the volume of a cube with multiple numbers of nodes. The geometrical, topological and structural aspects of the generated lattices are analysed and discussed. The results indicate that the method is capable of generating efficient space frame topologies that fill spatial envelopes

Genetic Programming to Optimise 3D Trajectories

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesTrajectory optimisation is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on non-intersection with any obstacles as well as predefined performance metrics. In the context of UAVs, the goal is to minimise the cost of the route, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques including evolutionary computation have been applied to trajectory optimisation with various degrees of success. This thesis explores the use of genetic programming (GP) to optimise trajectories in 3D space, by encoding 3D geographic trajectories as syntax trees representing a curve. A comprehensive review of the relevant literature is presented, covering the theory and techniques of GP, as well as the principles and challenges of 3D trajectory optimisation. The main contribution of this work is the development and implementation of a novel GP algorithm using function trees to encode 3D geographical trajectories. The trajectories are validated and evaluated using a realworld dataset and multiple objectives. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness. Finally, insights and recommendations for future research in this area are provided, highlighting the potential for GP to be applied to other complex optimisation problems in engineering and science