Scaling games to epic proportions

  • Authors:

  • Walker White;Alan Demers;Christoph Koch;Johannes Gehrke;Rajmohan Rajagopalan

  • Affiliations:

  • Cornell University, Ithaca, NY;Cornell University, Ithaca, NY;Saarland University, Saarbrücken, Germany;Cornell University, Ithaca, NY;Cornell University, Ithaca, NY

  • Venue:
  • Proceedings of the 2007 ACM SIGMOD international conference on Management of data
  • Year:
  • 2007

Quantified Score

Hi-index 0.01

Visualization

Abstract

We 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 have to 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. In this paper we make a first step towards truly scalable AI in computer games by modeling game AI as a data management problem. We present a highly expressive scripting language SGL that provides game designers and players with a data-driven AI scheme for customizing behavior for individual non-player characters. We use sophisticated query processing and indexing techniques to efficiently execute large numbers of SGL scripts, thus providing a framework for games with a truly epic number of non-player characters. Experiments show the efficacy of our solutions.