Use of fuzzy histograms to model the spatial distribution of objects in case-based reasoning

Authors:
Alan Davoust;Michael W. Floyd;Babak Esfandiari
Affiliations:
Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario;Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario;Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario
Venue:
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Year:
2008

Citing 3
Cited 1

Color indexing

International Journal of Computer Vision
Fuzzy Sets and Systems: Theory and Applications

Fuzzy Sets and Systems: Theory and Applications
Retrieving and reusing game plays for robot soccer

ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning

Considerations for Real-Time Spatially-Aware Case-Based Reasoning: A Case Study in Robotic Soccer Imitation

ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning

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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.