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Developed in 1990 by Ross Quinlan, [1] FOIL learns function-free Horn clauses, a subset of first-order predicate calculus.Given positive and negative examples of some concept and a set of background-knowledge predicates, FOIL inductively generates a logical concept definition or rule for the concept.
First-order logic also satisfies several metalogical theorems that make it amenable to analysis in proof theory, such as the Löwenheim–Skolem theorem and the compactness theorem. First-order logic is the standard for the formalization of mathematics into axioms, and is studied in the foundations of mathematics.
First-order arithmetic; First-order condition; First-order hold, a mathematical model of the practical reconstruction of sampled signals; First-order inclusion probability; First Order Inductive Learner, a rule-based learning algorithm; First-order reduction, a very weak type of reduction between two computational problems; First-order resolution
Models of scientific inquiry have two functions: first, to provide a descriptive account of how scientific inquiry is carried out in practice, and second, to provide an explanatory account of why scientific inquiry succeeds as well as it appears to do in arriving at genuine knowledge.
Inductive reasoning is any of various methods of reasoning in which broad generalizations or principles are derived from a body of observations. [1] [2] Inductive reasoning is in contrast to deductive reasoning (such as mathematical induction), where the conclusion of a deductive argument is certain, given the premises are correct; in contrast, the truth of the conclusion of an inductive ...
First-order logic is an example of a formal language. The logical positivists envisaged a similar scientific language. The logical positivists envisaged a similar scientific language. In addition to scientific theories, the language also included observation sentences ("the sun rises in the east"), definitions, and mathematical statements.
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 inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs of given inputs that it has not encountered. [1] Inductive bias is anything which makes the algorithm learn one pattern instead of another pattern (e.g., step-functions in decision trees instead of ...