Where do VSA's fit into AI?

John Langford discusses different aspect of machine learning in a post Languages of Learning:

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In addition there are languages related to various aspects of learning.

1. Reductions: A language for translating between varying real-world losses and core learning algorithm optimizations.
   
2. Feature Languages: Exactly how features are specified varies from on learning algorithm to another. Several people have been working on languages for features that cope with sparsity or the cross-product nature of databases.
   
3. Data interaction languages: The statistical query model of learning algorithms provides a standardized interface between data and learning algorithm.
   

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It seems VSA's fit right into the "Feature Languages" category, and they provide solutions to the problem of representing combinatorial features (which are very sparse).