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2008
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Use of Fuzzy Histograms to Model the Spatial Distribution of Objects in Case-Based Reasoning

9 years 20 days ago
Use of Fuzzy Histograms to Model the Spatial Distribution of Objects in Case-Based Reasoning
Abstract. 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. Key words: Case Based Reasoning, Fuzzy Histograms, Knowledge Representation, Soccer Simulation
Alan Davoust, Michael W. Floyd, Babak Esfandiari
Added 01 Jun 2010
Updated 01 Jun 2010
Type Conference
Year 2008
Where AI
Authors Alan Davoust, Michael W. Floyd, Babak Esfandiari
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