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.

Nostalgia videogames as playable game criticism

The aim of this paper is to consider the emergence of nostalgia videogames in the context of playable game criticism. Mirroring the development of the nostalgia film in cinema, an increasing number of developers are creating videogames that are evocative of past gaming forms, designs, and styles. The primary focus of this paper is to explore the extent to which these nostalgia videogames could be considered games-on-games: games that offer a critical view on game design and development, framed by the nostalgia and cultural memory of both gamers and game developers. Theories of pastiche and parody as applied to literature, film, and art are used to form a basis for the examination of recent nostalgia videogames, all of which demonstrate a degree of reflection on the videogame medium.