Heuristic Rule

A Heuristic Rule is a rule that is intended to achieve an approximate solution.

• AKA: Rule of Thumb.
• Context:
  • It can be used by a Heuristic Algorithm, such as a Heuristic Classification Algorithm.
  • …
• Example(s):
  • a Human Heuristic, such as: "A Penny Saved is a Penny Earned"
  • …
• Counter-Example(s):
  • a Trained Decision Tree.
• See: Heuristic Model, Problem Solving Capability, Hypothesis, Common Sense, Problem Solving, Educated Guess.

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References

2021

• (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Heuristic_(computer_science) Retrieved:2021-4-11.
  • In mathematical optimization and computer science, heuristic (from Greek εὑρίσκω "I find, discover") is a technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, accuracy, or precision for speed. In a way, it can be considered a shortcut.

    A heuristic function, also simply called a heuristic, is a function that ranks alternatives in search algorithms at each branching step based on available information to decide which branch to follow. For example, it may approximate the exact solution.

2014

• (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/heuristic Retrieved:2014-1-7.
  • Heuristic ( /hyʉˈrɪstɪk/; Greek: "Εὑρίσκω", "find” or "discover") refers to experience-based techniques for problem solving, learning, and discovery that give a solution which is not guaranteed to be optimal. Where the exhaustive search is impractical, heuristic methods are used to speed up the process of finding a satisfactory solution via mental shortcuts to ease the cognitive load of making a decision. Examples of this method include using a rule of thumb, an educated guess, an intuitive judgment, stereotyping, or common sense.

    In more precise terms, heuristics are strategies using readily accessible, though loosely applicable, information to control problem solving in human beings and machines.

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