Model-Driven AI for Games: Research Plan

Abstract

The field of game AI is largely industry driven, lacking an agreed upon formalism for AI representation. Ad-hoc scripting languages, simple finite state machines, behaviour trees, and planners are employed, but not in a fashion adhering to any standard. As a result, reuse is sparse between games and formal analysis techniques are undeveloped. As research for a Ph.D. thesis, we propose to show that a layered Statechart-based AI is a suitable formalism for Game AI, enabling the use of model-driven development techniques such as reuse and high-level analysis including model-checking. The fundamentally modular nature of this approach leads naturally to reuse as a fundamental component of the design process. Supported by a clearly defined formalism, useful behavioural analyses become possible, such as testing reactions to various inputs at design time. We also explore transformations at the modelling level to enable procedural generation, allowing rapid deployment of varying AIs. Additionally, such a model allows for the generation of efficient code that can be directly inserted into games. Tool support for reuse, generation, and analysis will be developed, then employed in creating an industrial scale AI, proving that this formalism is appropriate for industrial use.