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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.
While deductive logic allows one to arrive at a conclusion with certainty, inductive logic can only provide a conclusion that is probably true. [non-primary source needed] It is mistaken to frame the difference between deductive and inductive logic as one between general to specific reasoning and specific to general reasoning. This is a common ...
Mill's methods are five methods of induction described by philosopher John Stuart Mill in his 1843 book A System of Logic. [ 1 ] [ 2 ] They are intended to establish a causal relationship between two or more groups of data, analyzing their respective differences and similarities.
For example, when predicting how a person will react to a situation, inductive reasoning can be employed based on how the person reacted previously in similar circumstances. It plays an equally central role in the sciences , which often start with many particular observations and then apply the process of generalization to arrive at a universal ...
This definition is disputable (due to its lack of clarity. Ref: Oxford English dictionary: "induction ... 3. Logic the inference of a general law from particular instances." [clarification needed]) The definition given thus applies only when the "conclusion" is general. Two possible definitions of "inference" are:
Inductive logic started to take a clearer shape in the early 20th century in the work of William Ernest Johnson and John Maynard Keynes, and was further developed by Rudolf Carnap. Carnap introduced the distinction between pure and applied inductive logic, [ 1 ] and the modern Pure Inductive Logic evolves along the lines of the pure ...
The input to Aleph is background knowledge, specified as a logic program, a language bias in the form of mode declarations, as well as positive and negative examples specified as ground facts. [2] As output it returns a logic program which, together with the background knowledge, entails all of the positive examples and none of the negative ...
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 ...