Which features of a computer game contribute to the player’s enjoyment of it? How can we automatically generate interesting and satisfying playing experiences for a given game? These are the two key questions addressed in this dissertation. Player satisfaction in computer games depends on a variety of factors; here the focus is on the contribution of the behaviour and strategy of game opponents in predator/prey games. A quantitative metric of the ‘interestingness’ of opponent behaviours is defined based on qualitative considerations of what is enjoyable in such games, and a mathematical formulation grounded in observable data is derived. Using this metric, neural-network opponent controllers are evolved for dynamic game environments where limited inter-agent communication is used to drive spatial coordination of opponent teams. Given the complexity of the predator task, cooperative team behaviours are investigated. Initial candidates are generated using off-line learning procedures operating on minimal neural controllers with the aim of maximising opponent performance. These example controllers are then adapted using on-line (i.e. during play) learning techniques to yield opponents that provide games of high interest. The on-line learning methodology is evaluated using two dissimilar predator/prey games with a number of different computer player strategies. It exhibits generality across the two game test-beds and robustness to changes of player, initial opponent controller selected, and complexity of the game field. The interest metric is also evaluated by comparison with human judgement of game satisfaction in an experimental survey. A statistically significant number of players were asked to rank game experiences with a test-bed game using perceived interestingness and their ranking was compared with that of the proposed interest metric. The results show that the interest metric is consistent with human judgement of game satisfaction. Finally, the generality, limitations and potential of the proposed methodology and techniques are discussed, and other factors affecting the player’s satisfaction, such as the player’s own strategy, are briefly considered. Future directions building on the work described herein are presented and discussed.

This study explores how respondents perceive human-composed music and AI-computer-composed music. The aim was to find out if there is a negative bias against AI-computer-composed music. The research questions are 1. How is AI-computer-composed music perceived compared to human-composed music? 2. Are there prejudices towards AI-computer-composed music? If yes, what are the prejudices? Four participants took part in a qualitative experiment and a semi-structured interview. Two music pieces were used as artifacts, one was human-composed, and the AI-computer AIVA composed the other. The results showed that although the researchers have not revealed to the participants if they had chosen the AI-computer-composed song or the human-composed song as their favorite, all the participants strongly believed that their favorite song was human-composed. Thus, indicating a bias towards human-composed music The results also showed that the two music pieces were not perceived to have the same characteristics or evoke the same emotions; furthermore, there was some skepticism, whether an AI-computer-composed song could recall the same emotions as a human-composed song. However, none of the respondents explicitly expressed negativity towards AI-computer-composed music.

In unserem Vortrag stellen wir die verbesserte Version des vor zwei Jahren präsentierten Kanu KI Analysators vor. Die automatische Paddel- und Paddelwinkelerkennung ist mit einer Trefferquote von über 99% sehr robust geworden. Die Erkennung der Paddelposen des Technikleitbildes hat sich von 37% auf 60% verbessert. / In our presentation, we introduce the improved version of the Canoe AI Analyzer presented two years ago. The automatic paddle and paddle angle detection has become very robust with an accuracy rate of over 99%. The paddle pose detection of the technique guide has improved from 37% to 60%.