Development of Ayo Game Model for Handheld Devices

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dc.contributor.authorAyilara, Oluwatobi Adedamola
dc.date.accessioned2023-05-13T16:33:03Z
dc.date.available2023-05-13T16:33:03Z
dc.date.issued2015
dc.descriptionxii,77 Pen_US
dc.description.abstractIn this study, Ayo game model was designed, simulated and evaluated. This was with a view to optimizing resources on low prized handheld devices as existing models consume resources, making their deployment on handheld devices computationally challenging Data relating to Ayo game playing patterns were collected from human experts and from a computer simulated game involving Alpha-beta search algorithm and Random player. The data collected were analysed to extract key game's states (appropriate time to play the 'Odu'). The extracted data were used to train an unsupervised neural network- the Learning Vector Quantization (LVQ) network. The LVQ network was then combined with the Alpha-beta search algorithm to make the intelligent component of the Ayo game model (synthetic player). The model was designed using Unified Modelling Language (UML) and simulated in a MATLAB environment. The performance of the Ayo game model (LVQ with Alpha-beta search algorithm) against the Alpha-beta search algorithm and Random player (with randomized effect) was evaluated in terms of response time (computer’s turn) and the win percentage. The average number of games won by the Alpha-beta algorithm against Random (excluding games drawn) was 18% while the average number of games won by LVQ with Alpha-beta algorithm against Random (excluding drawn games) was 29%. The percentage increase in games won by LVQ with Alpha-beta search over the Alpha-beta search algorithm was approximately 40%. The average number of turns used by Alpha-beta search and LVQ with Alpha-beta players were 65.8 and 41, respectively, giving a 38% decrease in number of turns used by Ayo game model, thus resulting in shorter game's length. The average response times for Alpha-beta search algorithm and LVQ with Alpha-beta search were 14161ns and 27202ns, respectively. The result of performance evaluation of the model has shown that the model will run conveniently of any type of handheld devices.en_US
dc.identifier.citationAyilara,O.A(2016).Development of Ayo Game Model for Handheld Devices.Obafemi Awolowo University.en_US
dc.identifier.urihttps://ir.oauife.edu.ng/123456789/5277
dc.language.isoenen_US
dc.publisherObafemi Awolowo Universityen_US
dc.subjectAyo Gameen_US
dc.subjectOduen_US
dc.subjectAlpha-beta search algorithmen_US
dc.subjectLearning Vector Quantizationen_US
dc.subjecta MATLAB environmenten_US
dc.titleDevelopment of Ayo Game Model for Handheld Devicesen_US
dc.typeThesisen_US
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