Theory Recovery

Rupert Parson1, Khalid Khan1, and Stephen Muggleton2

1 Oxford University Computing Laboratory, UK
[rupert.parson,khalid.khan]@comlab.ox.ac.uk
2 Department of Computer Science, University of York, UK
stephen@cs.york.ac.uk



Abstract. In this paper we examine the problem of repairing incomplete
background knowledge using Theory Recovery. Repeat Learning under
ILP considers the problem of updating background knowledge in order to
progressively increase the performance of an ILP algorithm as it tackles
a sequence of related learning problems. Theory recovery is suggested as
a suitable mechanism. A bound is derived for the performance of theory
recovery in terms of the information content of the missing predicate definitions. 
Experiments are described that use the logical back-propagation
ability of Progol 5.0 to perform theory recovery. The experimental results
are consistent with the derived bound.
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