Theory Recovery

Authors:
Rupert Parson;Khalid Khan;Stephen Muggleton
Affiliations:
-;-;-
Venue:
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Year:
1999

Citing 5
Cited 0

Codes and cryptography

Codes and cryptography
A Prolog technology theorem prover: a new exposition and implementation in Prolog

Theoretical Computer Science - Selected papers on theoretical issues of design and implementation of symbolic computation systems
Learning from Positive Data

ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Learning Programs in the Event Calculus

ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Repeat Learning Using Predicate Invention

ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming

Quantified Score

Hi-index	0.00

Visualization

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.