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Symbolic Concept Acquisition

Symbolic Concept Acqusition (SCA) is a model of concept learning implemented in Soar. SCA was developed by Craig Miller. The version of SCA documented here has been updated for Soar8 by Bob Wray. The minor modifications made to SCA for compatibility with Soar8 are detailed in the documentation. This documentation also includes a conceptual overview of SCA, as well as specific documentation of the Soar production code.

SCA has also been extended to include knowledge to map novel or unknown values of features to known values. Novel features are often introduced in transfer tasks in psychological experiments. The solution proposed in this version of SCA is to allow novel values to be mapped to known values. This mapping process competes with abstraction. The result is that unknown values may be ignored (via abstraction) or mapped to a related value. (more information)

All the files needed for SCA are included in sca.zip. To provide a "working model", some additional files are included that can be used to run a simple model and trace the action of SCA. These files are located in the directory test-harness/. The test-harness productions set up a training instance for prediction (no feedback), then learning (feedback provided), then halt the model. You can step through the execution of this example to see how the abstraction process works, and the differences between prediction and training in the model. Several logfiles are included (links below) that also illustrate SCA with this test-harness.

Additional information about SCA:

Documented at line 1 of file SCA_source.soar


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