Principled animation of artificial intelligence algorithms

Abstract

Visualization is an important component of modern computing. By animating the course of an algorithm's temporal execution, many key features can be elucidated. The author has developed a general framework, termed Call-Graph Caching (CGC), for automating the construction of many complex AI algorithms. By incorporating visualization into CGC interpreters, principled animations can be automatically displayed as AI computations unfold. Systems that support the automation animation of AI algorithms must address these three design issues: how to represent AI data structures in a general, uniform way that leads to perspicuous animation and efficient redisplay; how to coordinate the succession of graphical events; and how to partition AI graphs to provide for separate, uncluttered displays. CGC provides a natural and effective solution to all these concerns. The author describes the CGC method, including detailed examples, and discusses why CGC works well for animation. He discusses the CACHE system, the CGC environment for AI algorithm animation. Finally, the author demonstrates the animation of several AI algorithms-RETE match, linear unification, arc consistency, chart parsing, and truth maintenance-all of which have been implemented in CACHE.