Mosts existing theory refinement systems are not incremental. However, any theory refinement system whose input and output theories are compatible can be used to incrementally assimilate data into an evolving theory. This is done by continually feeding its revised theory back in as its input theory. An incremental batch approach, in which the system assimilates a batch of examples at each step, seems most appropriate for existing theory revision systems. Experimental results with the EITHER theory refinement system demonstrate that this approach frequently increases efficiency without significantly decreasing the accuracy or the simplicity of the resulting theory. However, if the system produces bad initial changes to the theory based on only small amount of data, these bad revisions can ``snowball'' and result in an overall decrease in performance.

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In Proceedings of the 1992 AAAI Spring Symposium on Knowledge Assimilation, Standford, CA, March 1992.

@inproceedings{mooney:aaai-ka92, title={Batch versus Incremental Theory Refinement}, author={Raymond J. Mooney}, booktitle={Proceedings of the 1992 AAAI Spring Symposium on Knowledge Assimilation}, month={March}, address={Standford, CA}, url="http://www.cs.utexas.edu/users/ai-lab?mooney:aaai-ka92", year={1992} }