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International Journal of Computer Games Technology
Volume 2009, Article ID 318505, 9 pages
Research Article

Long Memory of Pathfinding Aesthetics

Computer Science Department, Marist College, Poughkeepsie, NY 12601, USA

Received 16 January 2009; Revised 30 March 2009; Accepted 22 June 2009

Academic Editor: Marc Price

Copyright © 2009 Ron Coleman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper investigates a new dynamic (i.e., space-time) model to measure aesthetic values in pathfinding for videogames. The results we report are important firstly because the artificial intelligence literature has given relatively little attention to aesthetic considerations in pathfinding. Secondly, those investigators who have studied aesthetics in pathfinding have relied largely on anecdotal arguments rather than metrics. Finally, in those cases where metrics have been used in the past, they show only that aesthetic paths are different. They provide no quantitative means to classify aesthetic outcomes. The model we develop here overcomes these deficiencies using rescaled range (R/S) analysis to estimate the Hurst exponent, 𝐻 . It measures long-range dependence (i.e., long memory) in stochastic processes and provides a novel well-defined mathematical classification for pathfinding. Indeed, the data indicates that aesthetic and control paths have statistically significantly distinct 𝐻 signatures. Aesthetic paths furthermore have more long memory than controls with an effect size that is large, more than three times that of an alternative approach. These conclusions will be of interest to researchers investigating games as well as other forms of entertainment, simulation, and in general nonshortest path motion planning.