Abstract
The learning system Progol5 and the underlying inference method of Bottom Generalisation are firmly established within Inductive Logic Programming (ILP). But despite their success, it is known that Bottom Generalisation, and therefore Progol5, are restricted to
finding hypotheses that lie within the semantics of Plotkin’s relative subsumption.
This paper exposes a previously unknown incompleteness of Progol5 with respect to Bottom Generalisation, and proposes a new approach, called Hybrid Abductive Inductive Learning, that integrates the ILP principles of Progol5 with Abductive Logic Programming (ALP).
A proof procedure is proposed, called HAIL, that not only overcomes this newly discovered incompleteness, but further generalises Progol5 by computing multiple clauses in response to a single seed example and deriving hypotheses outside Plotkin’s relative subsumption. A semantics is presented, called Kernel Generalisation, which extends that of Bottom
Generalisation and includes the hypotheses constructed by HAIL.
Translated title of the contribution | Hybrid Abductive Inductive Learning: A Generalisation of Progol |
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Original language | English |
Title of host publication | 13th International Conference on Inductive Logic Programming |
Publisher | Springer |
Publication status | Published - 2003 |
Bibliographical note
Other page information: 311-328Conference Proceedings/Title of Journal: 13th International Conference on Inductive Logic Programming
Other identifier: 2000713