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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 ...
Example of a logic model for a school-based self-management educational interventions for asthma in children and adolescents. Logic models are hypothesized descriptions of the chain of causes and effects leading to an outcome of interest (e.g. prevalence of cardiovascular diseases, annual traffic collision, etc).
Abductive logic programming (ALP) is a high-level knowledge-representation framework that can be used to solve problems declaratively, based on abductive reasoning. It extends normal logic programming by allowing some predicates to be incompletely defined, declared as abducible predicates. Problem solving is effected by deriving hypotheses on ...
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.
The well-founded semantics assigns a unique model to every general logic program. However, instead of only assigning propositions true or false, it adds a third value unknown for representing ignorance. [1] A simple example is the logic program that encodes two propositions a and b, and in which a must be true whenever b is not and vice versa:
First-order logic—also called predicate logic, predicate calculus, quantificational logic—is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantified variables over non-logical objects, and allows the use of sentences that contain variables.
An early example of answer set programming was the planning method proposed in 1997 by Dimopoulos, Nebel and Köhler. [3] [4] Their approach is based on the relationship between plans and stable models. [5] In 1998 Soininen and Niemelä [6] applied what is now known as answer set programming to the problem of product configuration. [4]
Probabilistic logic programming is a programming paradigm that combines logic programming with probabilities. Most approaches to probabilistic logic programming are based on the distribution semantics, which splits a program into a set of probabilistic facts and a logic program.