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. 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.
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- enInductive logic programming (ILP) is a subfield of symbolic artificial intelligence 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.
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- 28 November 2011
- 26 March 2014
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- enInductive logic programming (ILP) is a subfield of symbolic artificial intelligence 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. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples. The first full first-order implementation of inductive logic programming was Theorist in 1986. The term Inductive Logic Programming was first introduced in a paper by Stephen Muggleton in 1991. Muggleton also founded the annual international conference on Inductive Logic Programming, introduced the theoretical ideas of Predicate Invention, Inverse resolution, and Inverse entailment. Muggleton implemented Inverse entailment first in the PROGOL system. 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.
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- enInductive logic programming
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- Anti-unification (computer science)
- Bioinformatics
- Category:Inductive logic programming
- Clause normal form
- Commonsense reasoning
- Database
- Ehud Shapiro
- Entailment
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- First Order Inductive Learner
- Formal concept analysis
- Golem (ILP)
- Gordon Plotkin
- Ground expression
- Herbrand structure
- Horn clauses
- Inductive probability
- Inductive programming
- Inductive reasoning
- Inverse resolution
- Kolmogorov complexity
- Literal (mathematical logic)
- Logic programming
- Mathematical induction
- Natural language processing
- PROGOL
- Prolog
- Statistical relational learning
- Stephen Muggleton
- Symbolic artificial intelligence
- Version space learning
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- Inductive logic programming
- Induktive logische Programmierung
- Induktivní logické programování
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- Programação em lógica indutiva
- Programación lógica inductiva
- Programmation logique inductive
- Programmazione logica induttiva
- Q1464197
- TfF1
- Tümevarımlı mantık programlama
- Индуктивное логическое программирование
- Индуктивно логичко програмирање
- برمجة منطقية استقرائية
- برنامهسازی منطقی استنتاجی
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