Abstract
Function induction is a major component of many learning
systems. Its purpose is to extract information in the form of a functional
relationship from a given set of examples. Since learning systems
strive for generality, search emerges as the best candidate for tackling
the task of function induction. However, it is inevitably less efficient
than specialized problem-solving methods. Previous researchers have
sacrificed generality by developing strategies that utilize
domain-specific knowledge to improve efficiency.
This thesis presents a different approach, the "equal-value" method, which
directly improves search performance while maintaining its generality. The
result is a new search strategy that is both general and efficient.
Experiments suggest that, in the case of numeric functions, performance can
increase by several orders of magnitude compared to generic exhaustive
search. While the strategy was developed specifically to address the function
induction problem, it is possible that a similar approach applies to other
induction problems. In any case, the equal-value search provides a powerful
new technique for general function induction.
Notes
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