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