Inductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. 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. 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.

History

Building on earlier work on Inductive inference, Gordon Plotkin was the first to formalise induction in a clausal setting around 1970, adopting an approach of generalising from examples.^[1][2] In 1981, Ehud Shapiro introduced several ideas that would shape the field in his new approach of model inference, an algorithm employing refinement and backtracing to search for a complete axiomatisation of given examples.^[1][3] His first implementation was the Model Inference System in 1981:^[4][5] a Prolog program that inductively inferred Horn clause logic programs from positive and negative examples.^[1] The term Inductive Logic Programming was first introduced in a paper by Stephen Muggleton in 1990, defined as the intersection of machine learning and logic programming.^[1] Muggleton and Wray Buntine introduced predicate invention and inverse resolution in 1988.^[1][6]

Several inductive logic programming systems that proved influential appeared in the early 1990s. FOIL, introduced by Ross Quinlan in 1990^[7] was based on upgrading propositional learning algorithms AQ and ID3.^[8] Golem, introduced by Muggleton and Feng in 1990, went back to a restricted form of Plotkin's least generalisation algorithm.^[8][9] The Progol system, introduced by Muggleton in 1995, first implemented inverse entailment, and inspired many later systems.^[8][10][11] Aleph, a descendant of Progol introduced by Ashwin Srinivasan in 2001, is still one of the most widely used systems as of 2022^[update].^[10]

At around the same time, the first practical applications emerged, particularly in bioinformatics, where by 2000 inductive logic programming had been successfully applied to drug design, carcinogenicity and mutagenicity prediction, and elucidation of the structure and function of proteins.^[12] Unlike the focus on automatic programming inherent in the early work, these fields used inductive logic programming techniques from a viewpoint of relational data mining. The success of those initial applications and the lack of progress in recovering larger traditional logic programs shaped the focus of the field.^[13]

Recently, classical tasks from automated programming have moved back into focus, as the introduction of meta-interpretative learning makes predicate invention and learning recursive programs more feasible. This technique was pioneered with the Metagol system introduced by Muggleton, Dianhuan Lin, Niels Pahlavi and Alireza Tamaddoni-Nezhad in 2014.^[14] This allows ILP systems to work with fewer examples, and brought successes in learning string transformation programs, answer set grammars and general algorithms.^[15]

Setting

Inductive logic programming has adopted several different learning settings, the most common of which are learning from entailment and learning from interpretations.^[16] In both cases, the input is provided in the form of background knowledge B, a logical theory (commonly in the form of clauses used in logic programming), as well as positive and negative examples, denoted ${\textstyle E^{+}}$ and ${\textstyle E^{-}}$ respectively. The output is given as a hypothesis H, itself a logical theory that typically consists of one or more clauses.

The two settings differ in the format of examples presented.

Learning from entailment

As of 2022^[update], learning from entailment is by far the most popular setting for inductive logic programming.^[16] In this setting, the positive and negative examples are given as finite sets ${\textstyle E^{+}}$ and ${\textstyle E^{-}}$ of positive and negated ground literals, respectively. A correct hypothesis H is a set of clauses satisfying the following requirements, where the turnstile symbol ${\displaystyle \models }$ stands for logical entailment:^[16][17][18]

Completeness requires any generated hypothesis h to explain all positive examples ${\textstyle E^{+}}$, and consistency forbids generation of any hypothesis h that is inconsistent with the negative examples ${\textstyle E^{-}}$, both given the background knowledge B.

In Muggleton's setting of concept learning,^[19] "completeness" is referred to as "sufficiency", and "consistency" as "strong consistency". Two further conditions are added: "Necessity", which postulates that B does not entail ${\textstyle E^{+}}$, does not impose a restriction on h, but forbids any generation of a hypothesis as long as the positive facts are explainable without it. . "Weak consistency", which states that no contradiction can be derived from ${\textstyle B\land H}$, forbids generation of any hypothesis h that contradicts the background knowledge B. Weak consistency is implied by strong consistency; if no negative examples are given, both requirements coincide. Weak consistency is particularly important in the case of noisy data, where completeness and strong consistency cannot be guaranteed.^[19]

Learning from interpretations

In learning from interpretations, the positive and negative examples are given as a set of complete or partial Herbrand structures, each of which are themselves a finite set of ground literals. Such a structure e is said to be a model of the set of clauses ${\textstyle B\cup H}$ if for any substitution ${\textstyle \theta }$ and any clause ${\textstyle \mathrm {head} \leftarrow \mathrm {body} }$ in ${\textstyle B\cup H}$ such that ${\textstyle \mathrm {body} \theta \subseteq e}$, ${\displaystyle \mathrm {head} \theta \subseteq e}$ also holds. The goal is then to output a hypothesis that is complete, meaning every positive example is a model of ${\textstyle B\cup H}$, and consistent, meaning that no negative example is a model of ${\textstyle B\cup H}$.^[16]

Approaches to ILP

An inductive logic programming system is a program that takes as an input logic theories ${\displaystyle B,E^{+},E^{-}}$ and outputs a correct hypothesis H with respect to theories ${\displaystyle B,E^{+},E^{-}}$. A system is complete if and only if for any input logic theories ${\displaystyle B,E^{+},E^{-}}$ any correct hypothesis H with respect to these input theories can be found with its hypothesis search procedure. Inductive logic programming systems can be roughly divided into two classes, search-based and meta-interpretative systems.

Search-based systems exploit that the space of possible clauses forms a complete lattice under the subsumption relation, where one clause ${\textstyle C_{1}}$ subsumes another clause ${\textstyle C_{2}}$ if there is a substitution ${\textstyle \theta }$ such that ${\textstyle C_{1}\theta }$, the result of applying ${\textstyle \theta }$ to ${\textstyle C_{1}}$, is a subset of ${\textstyle C_{2}}$. This lattice can be traversed either bottom-up or top-down.

Bottom-up search

Bottom-up methods to search the subsumption lattice have been investigated since Plotkin's first work on formalising induction in clausal logic in 1970.^[1][20] Techniques used include least general generalisation, based on anti-unification, and inverse resolution, based on inverting the resolution inference rule.

Least general generalisation

A least general generalisation algorithm takes as input two clauses ${\textstyle C_{1}}$ and ${\textstyle C_{2}}$ and outputs the least general generalisation of ${\textstyle C_{1}}$ and ${\textstyle C_{2}}$, that is, a clause ${\textstyle C}$ that subsumes ${\textstyle C_{1}}$ and ${\textstyle C_{2}}$, and that is subsumed by every other clause that subsumes ${\textstyle C_{1}}$ and ${\textstyle C_{2}}$. The least general generalisation can be computed by first computing all selections from ${\textstyle C}$ and ${\textstyle D}$, which are pairs of literals ${\displaystyle (L,M)\in <!--\; \in \; -->(C_1,C_2}$Zdroj:https://en.wikipedia.org?pojem=Inductive_logic_programming
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