Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.768548
Title: Automating game-design and game-agent balancing through computational intelligence
Author: Morosan, Mihail
ISNI:       0000 0004 7654 5576
Awarding Body: University of Essex
Current Institution: University of Essex
Date of Award: 2019
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Abstract:
Game design has been a staple of human ingenuity and innovation for as long as games have been around. From sports, such as football, to applying game mechanics to the real world, such as reward schemes in shops, games have impacted the world in surprising ways. The process of developing games can, and should, be aided by automated systems, as machines have proven capable of finding innovative ways of complementing human intuition and inventiveness. When man and machine co-operate, better products are created and the world has only to benefit. This research seeks to find, test and assess methods of using genetic algorithms to human-led game balancing tasks. From tweaking difficulty to optimising pacing, to directing an intelligent agent's behaviour, all these can benefit from an evolutionary approach and save a game designer many hours, if not days, of work based on trial and error. Furthermore, to improve the speed of any developed GAs, predictive models have been designed to aid the evolutionary process in finding better solutions faster. While these techniques could be applied on a wider variety of tasks, they have been tested almost exclusively on game balance problems. The major contributions are in defining the main challenges of game balance from an academic perspective, proposing solutions for better cooperation between the academic and the industrial side of games, as well as technical improvements to genetic algorithms applied to these tasks. Results have been positive, with success found in both academic publications and industrial cooperation.
Supervisor: Not available Sponsor: IGGI
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.768548  DOI: Not available
Keywords: NX Arts in general ; QA75 Electronic computers. Computer science
Share: