Hybrid Abductive Inductive Learning: A Generalisation of Progol

Oliver Ray, Krysia Broda, Alessandra Russo, T. Horvath, A. Yamamoto

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

23 Citations (Scopus)

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.
Original languageEnglish
Title of host publication13th International Conference on Inductive Logic Programming
PublisherSpringer
Publication statusPublished - 2003

Bibliographical note

Other page information: 311-328
Conference Proceedings/Title of Journal: 13th International Conference on Inductive Logic Programming
Other identifier: 2000713

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    Ray, O., Broda, K., Russo, A., Horvath, T., & Yamamoto, A. (2003). Hybrid Abductive Inductive Learning: A Generalisation of Progol. In 13th International Conference on Inductive Logic Programming Springer. http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2000713