Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach
<|reference_start|>Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach: Theory revision integrates inductive learning and background knowledge by combining training examples with a coarse domain theory to produce a more accurate theory. There are two challenges that theory revision and other theory-guided systems face. First, a representation language appropriate for the initial theory may be inappropriate for an improved theory. While the original representation may concisely express the initial theory, a more accurate theory forced to use that same representation may be bulky, cumbersome, and difficult to reach. Second, a theory structure suitable for a coarse domain theory may be insufficient for a fine-tuned theory. Systems that produce only small, local changes to a theory have limited value for accomplishing complex structural alterations that may be required. Consequently, advanced theory-guided learning systems require flexible representation and flexible structure. An analysis of various theory revision systems and theory-guided learning systems reveals specific strengths and weaknesses in terms of these two desired properties. Designed to capture the underlying qualities of each system, a new system uses theory-guided constructive induction. Experiments in three domains show improvement over previous theory-guided systems. This leads to a study of the behavior, limitations, and potential of theory-guided constructive induction.<|reference_end|>
@article{donoho1995rerepresenting,
    title={Rerepresenting and Restructuring Domain Theories: A Constructive
  Induction Approach},
    author={S. K. Donoho, L. A. Rendell},
    journal={Journal of Artificial Intelligence Research, Vol 2, (1995),
  411-446},
    year={1995},
    archivePrefix={arXiv},
    eprint={cs/9504101},
    primaryClass={cs.AI}
}