Issues Deriving from Chunking in Soar

• Utility Problem

  Chunking is invoked whenever an impasse is resolved, regardless of the utility of the learned rules. High match cost utility in Soar results in expensive chunks. Over-Specific chunks have low application frequency. Chunk creation is normally of sufficient benefit since the chunk represents the knowledge required to avoids the generation of an impasse should the same general situation occur.

  • Over-Specific Chunks

    Dependency analysis results in the inclusion of every working memory element accessed during impasse resolution that existed prior to generation of the impasse. Thus, the choice of representation is critical for avoiding these chunks. For example, in general one would want to only test for the presence of an attribute instead of a value whenever possible in problem solving to avoid creating chunks that were dependent upon specific values exclusively (i.e., no generalization).

  • Expensive Chunks

    Chunks that depend only upon attributes can match to a combinatorial number of values (an effect resulting from multi-attribute matching). This may cause a severe slow-down in matching, regardless of the efficiency of the matcher (assuming the standard implementation on a serial computer). The generation of expensive chunks may be avoided by a careful choice of the underlying representation, resulting in the generation of of a large (but not combinatorial) number of 'cheap' chunks. This solution is dependent upon the continued insignificance of the average growth effect.

• Tracing through Negations

  The outline of dependency analysis that was presented in this document was necessarily superficial. One complication to the procedure as it was presented is that Soar can test for the absence of a particular piece of information as well as its presence. This is called a negated condition. Negated conditions represent defeasibility in Soar's problem solving: the addition of more information (such as that represented by the negated condition) can cause the generation of a different result. Several mechanisms have been proposed for tracing these negations but no complete, computationally-feasible procedure has been found. Thus, problem solving that involved negated conditions is a source of over-general chunks.

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