Inductive logic programming

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. 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. His first implementation was the Model Inference System in 1981: a Prolog program that inductively inferred Horn clause logic programs from positive and negative examples. 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. Muggleton and Wray Buntine introduced predicate invention and inverse resolution in 1988.

Several inductive logic programming systems that proved influential appeared in the early 1990s. FOIL, introduced by Quinlan in 1990 was based on upgrading propositional learning algorithms AQ and ID3. Golem, introduced by Muggleton and Feng in 1992, went back to a restricted form of Plotkin's least generalisation algorithm. The PROGOL system, introduced by Muggleton in 1995, first implemented inverse entailment.

Formal definition

The background knowledge is given as a logic theory B, commonly in the form of Horn clauses used in logic programming. The positive and negative examples are given as a conjunction $E^{+}$ and $E^{-}$ of unnegated and negated ground literals, respectively. A correct hypothesis h is a logic proposition satisfying the following requirements.

${\begin{array}{llll}{\text{Necessity:}}&B&\not \models &E^{+}\\{\text{Sufficiency:}}&B\land h&\color {blue}{\models }&E^{+}\\{\text{Weak consistency:}}&B\land h&\not \models &{\textit {false}}\\{\text{Strong consistency:}}&B\land h\land E^{-}&\not \models &{\textit {false}}\end{array}}$

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

Example

Assumed family relations in section "Example"

The following well-known example about learning definitions of family relations uses the abbreviations

par: parent, fem: female, dau: daughter, g: George, h: Helen, m: Mary, t: Tom, n: Nancy, and e: Eve.

It starts from the background knowledge (cf. picture)

${\textit {par}}(h,m)\land {\textit {par}}(h,t)\land {\textit {par}}(g,m)\land {\textit {par}}(t,e)\land {\textit {par}}(n,e)\land {\textit {fem}}(h)\land {\textit {fem}}(m)\land {\textit {fem}}(n)\land {\textit {fem}}(e)$,

the positive examples

${\textit {dau}}(m,h)\land {\textit {dau}}(e,t)$,

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

Plotkin's "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 dau.

This approach uses the following steps.

• Relativize each positive example literal with the complete background knowledge:

  ${\begin{aligned}{\textit {dau}}(m,h)\leftarrow {\textit {par}}(h,m)\land {\textit {par}}(h,t)\land {\textit {par}}(g,m)\land {\textit {par}}(t,e)\land {\textit {par}}(n,e)\land {\textit {fem}}(h)\land {\textit {fem}}(m)\land {\textit {fem}}(n)\land {\textit {fem}}(e)\\{\textit {dau}}(e,t)\leftarrow {\textit {par}}(h,m)\land {\textit {par}}(h,t)\land {\textit {par}}(g,m)\land {\textit {par}}(t,e)\land {\textit {par}}(n,e)\land {\textit {fem}}(h)\land {\textit {fem}}(m)\land {\textit {fem}}(n)\land {\textit {fem}}(e)\end{aligned}}$,

• Convert into clause normal form:

  ${\begin{aligned}{\textit {dau}}(m,h)\lor \lnot {\textit {par}}(h,m)\lor \lnot {\textit {par}}(h,t)\lor \lnot {\textit {par}}(g,m)\lor \lnot {\textit {par}}(t,e)\lor \lnot {\textit {par}}(n,e)\lor \lnot {\textit {fem}}(h)\lor \lnot {\textit {fem}}(m)\lor \lnot {\textit {fem}}(n)\lor \lnot {\textit {fem}}(e)\\{\textit {dau}}(e,t)\lor \lnot {\textit {par}}(h,m)\lor \lnot {\textit {par}}(h,t)\lor \lnot {\textit {par}}(g,m)\lor \lnot {\textit {par}}(t,e)\lor \lnot {\textit {par}}(n,e)\lor \lnot {\textit {fem}}(h)\lor \lnot {\textit {fem}}(m)\lor \lnot {\textit {fem}}(n)\lor \lnot {\textit {fem}}(e)\end{aligned}}$,

• Anti-unify each compatible pair of literals:
  • ${\textit {dau}}(x_{me},x_{ht})$ from ${\textit {dau}}(m,h)$ and ${\textit {dau}}(e,t)$,
  • $\lnot {\textit {par}}(x_{ht},x_{me})$ from $\lnot {\textit {par}}(h,m)$ and $\lnot {\textit {par}}(t,e)$,
  • $\lnot {\textit {fem}}(x_{me})$ from $\lnot {\textit {fem}}(m)$ and $\lnot {\textit {fem}}(e)$,
  • $\lnot {\textit {par}}(g,m)$ from $\lnot {\textit {par}}(g,m)$ and $\lnot {\textit {par}}(g,m)$, similar for all other background-knowledge literals
  • $\lnot {\textit {par}}(x_{gt},x_{me})$ from $\lnot {\textit {par}}(g,m)$ and $\lnot {\textit {par}}(t,e)$, and many more negated literals
• Delete all negated literals containing variables that don't occur in a positive literal:
  • after deleting all negated literals containing other variables than $x_{me},x_{ht}$, only ${\textit {dau}}(x_{me},x_{ht})\lor \lnot {\textit {par}}(x_{ht},x_{me})\lor \lnot {\textit {fem}}(x_{me})$ remains, together with all ground literals from the background knowledge
• Convert clauses back to Horn form:
  • ${\textit {dau}}(x_{me},x_{ht})\leftarrow {\textit {par}}(x_{ht},x_{me})\land {\textit {fem}}(x_{me})\land ({\text{all background knowledge facts}})$

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

Concerning the above requirements, "Necessity" was satisfied because the predicate dau 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 h, since it, together with ${\textit {par}}(h,m)\land {\textit {fem}}(m)$ from the background knowledge, implies the first positive example ${\textit {dau}}(m,h)$, and similarly h and ${\textit {par}}(t,e)\land {\textit {fem}}(e)$ from the background knowledge implies the second positive example ${\textit {dau}}(e,t)$. "Weak consistency" is satisfied by h, since h holds in the (finite) Herbrand structure described by the background knowledge; similar for "Strong consistency".

The common definition of the grandmother relation, viz. ${\textit {gra}}(x,z)\leftarrow {\textit {fem}}(x)\land {\textit {par}}(x,y)\land {\textit {par}}(y,z)$, cannot be learned using the above approach, since the variable y 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

An inductive logic programming system is a program that takes as an input logic theories $B,E^{+},E^{-}$ and outputs a correct hypothesis H with respect to theories $B,E^{+},E^{-}$. Search-based ILP systems consist 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 if and only if for any input logic theories $B,E^{+},E^{-}$ any correct hypothesis H with respect to these input theories can be found with its hypothesis search procedure.

Hypothesis search

The ILP systems Progol, Hail and Imparo find a hypothesis H using the principle of the inverse entailment for theories B, E, H: $B\land H\models E\iff B\land \neg E\models \neg H$. First they construct an intermediate theory F called a bridge theory satisfying the conditions $B\land \neg E\models F$ and $F\models \neg H$. Then as $H\models \neg F$, they generalize the negation of the bridge theory F with anti-entailment. However, the operation of anti-entailment is computationally more expensive since it is highly nondeterministic. 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. On the other hand, Imparo is complete by both anti-entailment procedure and its extended inverse subsumption procedure.

Implementations

• 1BC and 1BC2: first-order naive Bayesian classifiers:
• ACE (A Combined Engine)
• Aleph
• Atom Archived 2014-03-26 at the Wayback Machine
• Claudien
• DL-Learner
• DMax
• FastLAS (Fast Learning from Answer Sets)
• FOIL (First Order Inductive Learner)
• Golem
• ILASP (Inductive Learning of Answer Set Programs)
• Imparo
• Inthelex (INcremental THEory Learner from EXamples) Archived 2011-11-28 at the Wayback Machine
• Lime
• Metagol
• Mio
• MIS (Model Inference System) by Ehud Shapiro
• PROGOL
• RSD
• Warmr (now included in ACE)
• ProGolem

See also

• Commonsense reasoning
• Formal concept analysis
• Inductive reasoning
• Inductive programming
• Inductive probability
• Statistical relational learning
• Version space learning

Further reading

• Muggleton, S.; De Raedt, L. (1994). "Inductive Logic Programming: Theory and methods". The Journal of Logic Programming. 19–20: 629–679. doi:10.1016/0743-1066(94)90035-3.
• Lavrac, N.; Dzeroski, S. (1994). Inductive Logic Programming: Techniques and Applications. New York: Ellis Horwood. ISBN 978-0-13-457870-5. Archived from the original on 2004-09-06. Retrieved 2004-09-22.
• Visual example of inducing the grandparenthood relation by the Atom system. http://john-ahlgren.blogspot.com/2014/03/inductive-reasoning-visualized.html Archived 2014-03-26 at the Wayback Machine