article · ACM SIGART Bulletin · 1994

Inductive logic programming

Cited by 70Open access

Abstract

Inductive Logic Programming (ILP) is a research area which investigates the construction of first-order definite clause theories from examples and background knowledge. ILP systems have been applied successfully in a number of real-world domains. These include the learning of structure-activity rules for drug design, finite-element mesh design rules, rules for primary-secondary prediction of protein structure and fault diagnosis rules for satellites. There is a well established tradition of learning-in-the-limit results in ILP. Recently some results within Valiant's PAC-learning framework have also been demonstrated for ILP systems. In this paper it is argued that algorithms can be directly derived from the formal specifications of ILP. This provides a common basis for Inverse Resolution, Explanation-Based Learning, Abduction and Relative Least General Generalisation. A new general-purpose, efficient approach to predicate invention is demonstrated. ILP is underconstrained by its logical specification. Therefore a brief overview of extra-logical constraints used in ILP systems is given. Some present limitations and research directions for the field are identified.

Cite this paper

APA 7

Muggleton, S. (1994). Inductive logic programming. ACM SIGART Bulletin, 5(1), 5–11. https://doi.org/10.1145/181668.181671

MLA 9

Muggleton, Stephen. “Inductive logic programming.” ACM SIGART Bulletin, vol. 5, no. 1, 1994, pp. 5–11, https://doi.org/10.1145/181668.181671.

Chicago

Muggleton, Stephen. 1994. “Inductive logic programming.” ACM SIGART Bulletin 5 (1): 5–11. https://doi.org/10.1145/181668.181671.

Harvard

Muggleton, S. (1994) ‘Inductive logic programming’, ACM SIGART Bulletin, 5(1), pp. 5–11. Available at: https://doi.org/10.1145/181668.181671.

BibTeX

@article{muggleton1994inductive,
  title = {{Inductive logic programming}},
  author = {Muggleton, Stephen},
  journal = {ACM SIGART Bulletin},
  volume = {5},
  number = {1},
  pages = {5--11},
  year = {1994},
  doi = {10.1145/181668.181671}
}

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