Abductive

Abductive logic programming Abductive logic programming is an extension of normal Logic Programming that allows some predicates, declared as abducible predicates, to be "open" or undefined.

Abduction or abductive reasoning is third component of logic and involves something that allows us to move from data to an argument.

Abductive logic programming has been used for fault diagnosis, planning, natural language processing and machine learning.

Abductive logic programming is a computational framework that extends normal logic programming with abduction.

Abductive planning with the event calculus.

Abductive validation is common practice in hypothesis formation in science ; moreover, Peirce claims that it is a ubiquitous aspect of thought: Looking out my window this lovely spring morning, I see an azalea in full bloom.

Critique of arguments At the critical level Peirce examined the forms of abductive arguments (as discussed above), and came to hold that the hypothesis should economize explanation for plausibility in terms of the feasible and natural.

It is thus possible to perform abductive analysis in the presence of missing or incomplete input evidence, which normally results in degrees of uncertainty in the output conclusions.

Peirce argues that good abductive reasoning from P to Q involves not simply a determination that Q is sufficient for P, but also that Q is among the most economical explanations for P. Simplification and economy both call for that "leap" of abduction.

Peirce held that: 1. Hypothesis (abductive inference) is inference through an icon (also called a likeness).

Therefore, by abductive reasoning, the possibility that it rained last night is reasonable, although Peirce did not remain convinced that a single logical form covers all abduction.