Abstract
Walker White: Scaling Computer Games to Epic Proportions
Abstract
In this talk I will introduce scalability for computer games as the next frontier for techniques from data management. A very important aspect of computer games is the artificial intelligence (AI) of non- player characters. To create interesting AI in games today, developers or players can create complex, dynamic behavior for a very small number of characters, but neither the game engines nor the style of AI programming enables intelligent behavior that scales to a very large number of non-player characters.
I
will present a first step towards truly scalable AI in computer games by
modeling game AI as a data management problem. The presentation includes a
highly expressive scripting language SGL (for Scalable Gaming Language) that
provides game designers and players with a data-driven AI scheme for
customizing behavior for individual non-player characters. The use sophisticated query processing and
indexing techniques allows us to efficiently execute large numbers of SGL
scripts, thus providing a framework for games with a truly epic number of
non-player characters. I conclude with an outlook how our techniques can be
used to also achieve significant scalability in large-scale simulations.
This talk describes joint
work with Alan Demers (Cornell), Johannes Gehrke (Cornell) Christoph
Koch (