Younès Rabii

Rabii Younès, Cook Michael

A game's theme is an important part of its design -- it conveys narrative information, rhetorical messages, helps the player intuit strategies, aids in tutorialisation and more. Thematic elements of games are notoriously difficult for AI systems to understand and manipulate, however, and often rely on large amounts of hand-written interpretations and knowledge. In this paper we present a technique which connects game embeddings, a recent method for modelling game dynamics from log data, and word embeddings, which models semantic information about language. We explain two different approaches for using game embeddings in this way, and show evidence that game embeddings enhance the linguistic translations of game concepts from one theme to another, opening up exciting new possibilities for reasoning about the thematic elements of games in the future.

Younès Rabii, Michael Cook

Although procedural generation is popular among game developers, academic research on the topic has primarily focused on new applications, with some research into empirical analysis. In this paper we relate theoretical work in information theory to the generation of content for games. We prove that there is a relationship between the Kolomogorov complexity of the most complex artifact a generator can produce, and the size of that generator's possibility space. In doing so, we identify the limiting relationship between the knowledge encoded in a generator, the density of its output space, and the intricacy of the artifacts it produces. We relate our result to the experience of expert procedural generator designers, and illustrate it with some examples.