Long Memory of Pathfinding Aesthetics

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, . 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 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