Inductive Reasoning Task

An Inductive Reasoning Task is an Induction Task that requires a Logic Argument from a Set of Facts.

• AKA: Induction, Inductive Logic, Learning, Induce, Inductive, Inductive Reasoning.
• Context:
  • Input: a Fact Set.
  • Output: an Logic Argument (an Inductive Argument).
  • ... Reasoning Process ...
  • It can be supported by an Inductive Reasoning System that implements an Inductive Reasoning Algorithm.
  • It can produce a Predictive Function.
  • Its conclusions can change based on new information. (See: Non-Monotonic Reasoning)
  • It can involve the creation of a Test Set (to stress-test the argument).
  • It can make use of a Generalization operation.
• Example:
  • Induction("Socrates, Plato, and Aristotle were human and mortal.) => IF X is human THEN X is mortal; IF X is mortal THEN X is human.
• See: Abductive Reasoning, Deductive Reasoning, Learning Task, Statistical Inference.

References

2009

• WordNet
  • generalization: reasoning from detailed facts to general principles
• (Wikipedia, 2009) http://en.wikipedia.org/wiki/Inductive_reasoning
  • Induction or inductive reasoning, sometimes called inductive logic, is the process of reasoning in which the premises of an argument are believed to support the conclusion but do not entail it; i.e. they do not ensure its truth. Induction is a form of reasoning that makes generalizations based on individual instances.[1] It is used to ascribe properties or relations to types based on an observation instance (i.e., on a number of observations or experiences); or to formulate laws based on limited observations of recurring phenomenal patterns.
• http://en.wiktionary.org/wiki/inductive_reasoning
  • The process of making inferences based upon observed patterns, or simple repetition. Often used in reference to predictions about will happen or ...
• http://www.chass.ncsu.edu/langure/documents/modules/module_9/Glossary.doc
  • The process of reasoning in which the premises of an argument support the conclusion but do not ensure it. ...
• http://www.utexas.edu/academic/diia/assessment/iar/glossary.php
  • a logic model in which general principles are developed from the information gathered.
• http://www.numbernut.com/glossary/i.shtml
  • Making a generalization from specific cases; used to formulate a general rule after examining a pattern.
• http://clopinet.com/isabelle/Projects/ETH/Exam_Questions.html
  • Inference refers to the ability of a learning system, namely going from the "particular" (the examples) to the "general" (the predictive model). In the best of all worlds, we would not need to worry about model selection. Inference would be performed in a single step: we input training examples into a big black box containing all models, hyper-parameters, and parameters; outcomes the best possible trained model. In practice, we often use 2 levels of inference: we split the training data into a training set and a validation set. The training set serves the trains at the lower level (adjust the parameters of each model); the validation set serves to train at the higher level (select the model.) Nothing prevents us for using more than 2 levels. However, the price to pay will be to get smaller data sets to train with at each level.