Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782099
Title: Optimising kinematic systems using crowd-sourcing and genetic algorithms
Author: Henshall, Gareth
ISNI:       0000 0004 7967 7056
Awarding Body: Bangor University
Current Institution: Bangor University
Date of Award: 2019
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Abstract:
Procedural animation systems are capable of synthesising life-like organic motion automatically. However due to extensive parameterisation, tuning these systems can be very difficult. Not only are there potentially hundreds of interlinked parameters, the resultant animation can be very subjective, and the process is difficult to automate effectively. The research presented in this thesis is divided into three stages. Our first motivation is to examine whether artificially intelligent characters appear more or less human-like in virtual reality (VR). Our results indicate that there is a clear split in how we perceive an artificial character depending on viewing method and game type. Our second motivation is to assess whether anonymous individuals can anneal a procedurally animated creature towards a desired outcome. To do this we present an online system which used crowd-sourcing to direct a genetic algorithm. This methodology is further tested by asking users to interactively rate a population of virtual dolphins to a prescribed behavioural criterion. Our results show that within a few generations a group of users can successfully tune an animation system toward a desired behaviour. Our final motivation is to investigate if there are differences in animation and behavioural preference between observations made across 2D screens and VR. We describe a study where users tuned two sets of dolphin animation systems in parallel, one using a normal monitor and another using an Oculus Rift. Our results indicate that being immersed in VR leads to some key differences in preferred behaviour.
Supervisor: Ap Cenydd, Llyr ; Teahan, William Sponsor: Leverhulme Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.782099  DOI: Not available
Keywords: optimisation ; crowd-sourcing ; genetic algorithms
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