Date of Award

2007

Degree Type

Thesis

Degree Name

Bachelor of Science Honours

School

School of Computer and Security Science

Faculty

Faculty of Computing, Health and Science

First Advisor

Phillip Hingston

Abstract

This study aims to achieve higher replay and entertainment value in a game through human-like AI behaviour in computer controlled characters called bats. In order to achieve that, an artificial intelligence system capable of learning from observation of human player play was developed. The artificial intelligence system makes use of machine learning capabilities to control the state change mechanism of the bot. The implemented system was tested by an audience of gamers and compared against bats controlled by static scripts. The data collected was focused on qualitative aspects of replay and entertainment value of the game and subjected to quantitative analysis

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