Audio games: investigation of the potential through prototype development

This article discusses the potential of audio games based on the evaluation of three projects: a story-driven audio role-playing game (RPG), an interactive audiobook with RPG elements, and a set of casual sound-based games. The potential is understood, both in popularity and playability terms. The first factor is connected to the degree of players’ interest, while the second one to the degree of their engagement in sound-based game worlds. Although presented projects are embedded within the landscape of past and contemporary audio games and gaming platforms, the authors reach into the near future, concluding with possible development directions for this non-visual interactive entertainment.

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.