BitBox!: A case study interface for teaching real-time adaptive music composition for video games

Real-time adaptive music is now well-established as a popular medium, largely through its use in video game soundtracks. Commercial packages, such as fmod, make freely available the underlying technical methods for use in educational contexts, making adaptive music technologies accessible to students. Writing adaptive music, however, presents a significant learning challenge, not least because it requires a different mode of thought, and tutor and learner may have few mutual points of connection in discovering and understanding the musical drivers, relationships and structures in these works. This article discusses the creation of ‘BitBox!’, a gestural music interface designed to deconstruct and explain the component elements of adaptive composition through interactive play. The interface was displayed at the Dare Protoplay games exposition in Dundee in August 2014. The initial proof-of- concept study proved successful, suggesting possible refinements in design and a broader range of applications.

An investigation of two real time machine learning techniques that could enhance the adaptability of game AI agents

Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q-Learning and an NGram based system. It was found that N-Grams and QLearning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.

Adaptive cultural transmission biases in children and nonhuman primates

Comparative and evolutionary developmental analyses seek to discover the similarities and differences between humans and non-human species that illuminate both the evolutionary foundations of our nature that we share with other animals, and the distinctive characteristics that make human development unique. As our closest animal relatives, with whom we last shared common ancestry, non-human primates have beenparticularly important in this endeavour. Such studies that have focused on social learning, traditions, and culture have discovered much about the ‘how’ of social learning, concerned with key underlying processes such as imitation and emulation. One of the core discoveries is that the adaptive adjustment of social learning options to different contexts is not unique to human infants, therefore multiple new strands of research have begun to focus on more subtle questions about when, from whom, and why such learning occurs. Here we review illustrative studies on both human infants and young children and on non-human primates to identify the similarities shared more broadly across the primate order, and the apparent specialisms that distinguish human development. Adaptive biases in social learning discussed include those modulated by task comprehension, experience, conformity to majorities, and the age, skill, proficiency and familiarity of potential alternative cultural models.