A framework for the design and evaluation of magic tricks that utilises computational systems con gured with psychological constraints.

Abstract

A human magician blends science, psychology and performance to create a magical e ect. This thesis explores what can be achieved when that human intelligence is replaced or assisted by machine intelligence. Magical e ects are all in some form based on hidden mathematical, scienti c or psychological principles; the parameters controlling these underpinning techniques are hard for a magician to blend to maximise the magical e ect required. The complexity is often caused by interacting and con icting physical and psychological constraints that need to be optimally balanced. Normally this tuning is done by trial and error, combined with human intuitions. This thesis focuses on applying Arti- cial Intelligence methods to the creation, and optimisation, of magic tricks exploiting mathematical principles. Experimentally derived, crowd sourced, data about particular perceptual and cognitive features is used, combined with a model of the underlying mathematical process, to provide a psychologically valid metric to allow optimisation of magical impact. The thesis describes an optimisation framework that can be exibly applied to a range of di erent types of mathematics based tricks. Three case studies are presented as exemplars of the methodology at work, the outputs of which are: language and image based prediction and mind reading tricks, a magical jigsaw, and a mind reading card trick e ect. Each trick created is evaluated through testing at public engagement events, and in a laboratory environment. Further, a demonstration of the real world e cacy of the approach for professional performers is presented in the form of sales of the tricks in a reputable magic shop in London.