Compiling Prior Knowledge Into an Explicit Bias

Current theory-guided learning systems are inflexible, in that they are committed to performing one particular class of theory corrections; this is problematic because in some cases special-purpose theory-guided learning systems can dramatically outperform general-purpose ones. To address this problem, we describe a new system in which theory-guided learning is separated into two phases. The first phase is "theory intrepretation ", in which the initial theory is translated into an explicit description of the bias for an inductive learning system; we introduce antecedent description grammars as a language for explicitly representing this bias. The second phase is "grammatically biased learning", in which this bias is used to search for a hypothesis. We demonstrate empirically that this approach leads to a flexible learning system which can, by use of suitable translators, emulate several useful learning systems; we also argue that this architecture makes it easier for a user unfamiliar ..