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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Use of Fuzzy Histograms to Model the Spatial Distribution of Objects in Case-Based Reasoning
verfasst von : Alan Davoust, Michael W. Floyd, Babak Esfandiari
Erschienen in: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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In the context of the RoboCup Simulation League, we describe a new representation of a software agent’s visual perception (“scene”), well suited for case-based reasoning.
Most existing representations use either heterogeneous, manually selected features of the scene, or the raw list of visible objects, and use ad hoc similarity measures for CBR. Our representation is based on histograms of objects over a partition of the scene space. This method transforms a list of objects into an image-like representation with customizable granularity, and uses fuzzy logic to smoothen boundary effects of the partition. We also introduce a new similarity metric based on the Jaccard Coefficient, to compare scenes represented by such histograms.
We present our implementation of this approach in a case-based reasoning project, and experimental results showing highly efficient scene comparison.