"Hunt Takes Hare": Theming Games Through Game-Word Vector Translation
This addresses the challenge of thematic elements in games for AI systems, which often rely on hand-written interpretations, though it appears incremental as it builds on existing embedding methods.
The paper tackles the problem of AI systems understanding and manipulating game themes by connecting game embeddings (modeling game dynamics) with word embeddings (modeling semantic language information). It shows that game embeddings enhance linguistic translations of game concepts between themes, opening new possibilities for thematic reasoning.
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.