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Title: A framework for the design and evaluation of magic tricks that utilises computational systems configured with psychological constraints
Author: Williams, Howard Manning
ISNI:       0000 0004 5360 1408
Awarding Body: Queen Mary, University of London
Current Institution: Queen Mary, University of London
Date of Award: 2014
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A human magician blends science, psychology and performance to create a magical effect. This thesis explores what can be achieved when that human intelligence is replaced or assisted by machine intelligence. Magical effects are all in some form based on hidden mathematical, scientific or psychological principles; the parameters controlling these underpinning techniques are hard for a magician to blend to maximise the magical effect required. The complexity is often caused by interacting and conflicting 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 Artificial 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 flexibly applied to a range of different 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 effect. Each trick created is evaluated through testing at public engagement events, and in a laboratory environment. Further, a demonstration of the real world efficacy of the approach for professional performers is presented in the form of sales of the tricks in a reputable magic shop in London.
Supervisor: Not available Sponsor: Engineering and Physical Sciences Research Council (EPSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Electronic Engineering ; Computer Science ; Machine intelligence ; Artificial Intelligence