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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.
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 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 ...
The body of the while loop is the automaton step and the loop itself is the cycle of the automaton step. The program implements the work of a finite-state machine shown in the state diagram. The most important property of the program is that the automaton step code section is clearly localized.
A structurally recursive function uses the same idea to define a recursive function: "base cases" handle each minimal structure and a rule for recursion. Structural recursion is usually proved correct by structural induction; in particularly easy cases, the inductive step is often left out.
Axial coding in grounded theory is the process of relating codes (categories and concepts) to each other, via a combination of inductive and deductive thinking. According to Strauss and Corbin (1990, 1998) who propose the use of a "coding paradigm", the basic framework of generic relationships is understood to include categories related to:
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