The sub-symbolical representation often corresponds to a pattern that mirrors the way the biological sense organs describe the world. Sparse binary vectors can describe sub-symbolic representation, which can be efficiently stored in associative memories. According to the production system theory, we can define a geometrically based problemsolving model as a production system operating on sub-symbols. Our goal is to form a sequence of associations, which lead to a desired state represented by sub-symbols, from an initial state represented by sub-symbols. We define a simple and universal heuristics function, which takes into account the relationship between the vector and the corresponding similarity of the represented object or state in the real world.
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