Addressing complex design problems through inductive learning

Optimisation and related techniques are well suited to clearly defined problems involving systems that can be accurately simulated, but not to tasks in which the phenomena in question are highly complex or the problem ill-defined. These latter are typical of architecture and particularly creative design tasks, which therefore currently lack viable computational tools. It is argued that as design teams and construction projects of unprecedented scale are increasingly frequent, this is just where such optimisation and communication tools are most needed. This research develops a method by which to address complex design problems, by using inductive machine learning from example precedents either to approximate the behaviour of a complex system or to define objectives for its optimisation. Two design domains are explored. A structural problem of the optimisation of stiffness and mass of fine scale, modular space frames has relatively clearly defined goals, but a highly complex geometry of many interconnected members. A spatial problem of the layout of desks in the workplace addresses the social relationships supported by the pattern of their arrangement, and presents a design situation in which even the problem objectives are not known. These problems are chosen to represent a range of scales, types and sources of complexity against which the methods can be tested. The research tests two hypotheses in the context of these domains, relating to the simulation of a system and to communication between the designer and the machine. The first hypothesis is that the underlying structure and causes of a system’s behaviour must be understood to effectively predict or simulate its behaviour. This hypothesis is typical of modelling approaches in engineering. It is falsified by demonstrating that a function can be learned that models the system in question—either optimising of structural stiffness or determining desirable spatial patterns—without recourse to a bottom up simulation of that system. The second hypothesis is that communication of the behaviour of these systems to the machine requires explicit, a priori definitions and agreed upon conventions of meaning. This is typical of classical, symbolic approaches in artificial intelligence and still implicitly underlies computer aided design tools. It is falsified by a test equivalent to a test of linguistic competence, showing that the computer can form a concept of, and satisfy, a particular requirement that is implied only by ostensive communication by examples. Complex, ill-defined problems are handled in practice by hermeneutic, reflective processes, criticism and discussion. Both hypotheses involve discerning patterns caused by the complex structure from the higher level behaviour only, forming a predictive approximation of this, and using it to produce new designs. It is argued that as these abilities are the input and output requirements for a human designer to engage in the reflective design process, the machine can thus be provided with the appropriate interface to do so, resulting in a novel means of interaction with the computer in a design context. It is demonstrated that the designs output by the computer display both novelty and utility, and are therefore a potentially valuable contribution to collective creativity.