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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 + and respectively.
Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output examples or constraints.
This was considered a landmark application for inductive logic programming, as a general purpose inductive learner had discovered results that were both novel and meaningful to domain experts. [ 4 ] Progol proved very influential in the field, and the widely-used inductive logic programming system Aleph builds directly on Progol.
Aleph (A Learning Engine for Proposing Hypotheses) [1] is an inductive logic programming system introduced by Ashwin Srinivasan in 2001. As of 2022 it is still one of the most widely used inductive logic programming systems. It is based on the earlier system Progol. [2]
Inductive logic programming (ILP) is an approach to machine learning that induces logic programs as hypothetical generalisations of positive and negative examples. Given a logic program representing background knowledge and positive examples together with constraints representing negative examples, an ILP system induces a logic program that ...
Golem is an inductive logic programming algorithm developed by Stephen Muggleton and Cao Feng in 1990. [1] It uses the technique of relative least general generalisation proposed by Gordon Plotkin, leading to a bottom-up search through the subsumption lattice. [2]
Probabilistic inductive logic programming aims to learn probabilistic logic programs from data. This includes parameter learning, which estimates the probability annotations of a program while the clauses themselves are given by the user, and structure learning, in which the clauses themselves are induced by the probabilistic inductive logic ...
Inductive reasoning refers to a variety of methods of reasoning in which broad generalizations or principles are derived from a set of observations. [1] [2] Unlike deductive reasoning (such as mathematical induction), where the conclusion is certain, given the premises are correct, inductive reasoning produces conclusions that are at best probable, given the evidence provided.