Inductive logic programming

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Inductive logic programming (ILP) is a subfield of machine learning which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesised logic program which entails all the positive and none of the negative examples.

Schema: positive examples + negative examples + background knowledge => hypothesis.

Inductive logic programming is particularly useful in bioinformatics and natural language processing. Ehud Shapiro laid the theoretical foundation for inductive logic programming[1][2] and built its first implementation (Model Inference System) in 1981:[3] a Prolog program that inductively inferred logic programs from positive and negative examples. The term Inductive Logic Programming was first introduced[4] in a paper by Stephen Muggleton in 1991.[5] The term "inductive" here refers to philosophical (i.e. suggesting a theory to explain observed facts) rather than mathematical (i.e. proving a property for all members of a well-ordered set) induction.

Formal definition

The background knowledge is given as a logic theory , commonly in the form of Horn clauses used in logic programming. The positive and negative examples are given as a conjunction and of unnegated and negated ground literals, respectively. A correct hypothesis is a logic proposition satisfying the following requirements.[6]

Weak consistency:
Strong consistency:

"Necessity" does not impose a restriction on , but forbids any generation of a hypothesis as long as the positive facts are explainable without it. "Sufficiency" requires any generated hypothesis to explain all positive examples . "Weak consistency" forbids generation of any hypothesis that contradicts the background knowledge . "Strong consistency" also forbids generation of any hypothesis that is inconsistent with the negative examples , given the background knowledge ; it implies "Weak consistency"; if no negative examples are given, both requirements coincide. Džeroski [7] requires only "Sufficiency" (called "Completeness" there) and "Strong consistency".


Assumed family relations in section "Example"

The following well-known example about learning definitions of family relations uses the abbreviations , , , , , , , , and . It starts from the background knowledge (cf. picture)


the positive examples


and the trivial proposition to denote the absence of negative examples.

Plotkin's [8][9] "relative least general generalization (rlgg)" approach to inductive logic programming shall be used to obtain a suggestion about how to formally define the daughter relation .

This approach uses the following steps.

The resulting Horn clause is the hypothesis obtained by the rlgg approach. Ignoring the background knowledge facts, the clause informally reads " is called a daughter of if is the parent of and is female", which is a commonly accepted definition.

Concerning the above requirements, "Necessity" was satisfied because the predicate doesn't appear in the background knowledge, which hence cannot imply any property containing this predicate, such as the positive examples are. "Sufficiency" is satisfied by the computed hypothesis , since it, together with from the background knowledge, implies the first positive example , and similarly and from the background knowledge implies the second positive example . "Weak consistency" is satisfied by , since holds in the (finite) Herbrand structure described by the background knowledge; similar for "Strong consistency".

The common definition of the grandmother relation, viz. , cannot be learned using the above approach, since the variable occurs in the clause body only; the corresponding literals would have been deleted in the 4th step of the approach. To overcome this flaw, that step has to be modified such that it can be parametrized with different literal post-selection heuristics. Historically, the GOLEM implementation is based on the rlgg approach.

Inductive Logic Programming system

Inductive Logic Programming system is a program that takes as an input logic theories and outputs a correct hypothesis wrt theories An algorithm of an ILP system consists of two parts: hypothesis search and hypothesis selection. First a hypothesis is searched with an inductive logic programming procedure, then a subset of the found hypotheses (in most systems one hypothesis) is chosen by a selection algorithm. A selection algorithm scores each of the found hypotheses and returns the ones with the highest score. An example of score function include minimal compression length where a hypothesis with a lowest Kolmogorov complexity has the highest score and is returned. An ILP system is complete iff for any input logic theories any correct hypothesis wrt to these input theories can be found with its hypothesis search procedure.

Hypothesis search

Modern ILP systems like Progol,[5] Hail [12] and Imparo [13] find a hypothesis using the principle of the inverse entailment[5] for theories , , : . First they construct an intermediate theory called a bridge theory satisfying the conditions and . Then as , they generalize the negation of the bridge theory with the anti-entailment.[14] However, the operation of the anti-entailment since being highly non-deterministic is computationally more expensive. Therefore an alternative hypothesis search can be conducted using the operation of the inverse subsumption (anti-subsumption) instead which is less non-deterministic than anti-entailment.

Questions of completeness of a hypothesis search procedure of specific ILP system arise. For example, Progol's hypothesis search procedure based on the inverse entailment inference rule is not complete by Yamamoto's example.[15] On the other hand, Imparo is complete by both anti-entailment procedure [16] and its extended inverse subsumption [17] procedure.


See also


  1. Shapiro, Ehud Y. Inductive inference of theories from facts, Research Report 192, Yale University, Department of Computer Science, 1981. Reprinted in J.-L. Lassez, G. Plotkin (Eds.), Computational Logic, The MIT Press, Cambridge, MA, 1991, pp. 199–254.
  2. Shapiro, Ehud Y. (1983). Algorithmic program debugging. Cambridge, Mass: MIT Press. ISBN 0-262-19218-7
  3. Shapiro, Ehud Y. "The model inference system." Proceedings of the 7th international joint conference on Artificial intelligence-Volume 2. Morgan Kaufmann Publishers Inc., 1981.
  4. Luc De Raedt. A Perspective on Inductive Logic Programming. The Workshop on Current and Future Trends in Logic Programming, Shakertown, to appear in Springer LNCS, 1999. CiteSeerX: Template:Url
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  10. i.e. sharing the same predicate symbol and negated/unnegated status
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  12. Ray, O., Broda, K., & Russo, A. M. (2003). Hybrid abductive inductive learning. In LNCS: Vol. 2835. Pro- ceedings of the 13th international conference on inductive logic programming (pp. 311–328). Berlin: Springer.
  13. Kimber, T., Broda, K., & Russo, A. (2009). Induction on failure: learning connected Horn theories. In LNCS: Vol. 5753. Proceedings of the 10th international conference on logic programing and nonmonotonic reasoning (pp. 169–181). Berlin: Springer.
  14. Yoshitaka Yamamoto, Katsumi Inoue, and Koji Iwanuma. Inverse subsumption for complete explana- tory induction. Machine learning, 86(1):115–139, 2012.
  15. Akihiro Yamamoto. Which hypotheses can be found with inverse entailment? In Inductive Logic Programming, pages 296–308. Springer, 1997.
  16. 16.0 16.1 Timothy Kimber. Learning definite and normal logic programs by induction on failure. PhD thesis, Imperial College London, 2012.
  17. David Toth (2014). Imparo is complete by inverse subsumption. arXiv:1407.3836
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Further reading


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