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{{Short description|An approach to problem-solving or learning that employs a practical method not guaranteed to be optimal or perfect, but sufficient for reaching an immediate goal.}}
{{Short description|An approach to problem-solving or learning that employs a practical method not guaranteed to be optimal or perfect, but sufficient for reaching an immediate goal.}}
{{About|the problem-solving technique|other uses|Heuristic (disambiguation)}}
A '''heuristic''' is a practical approach to problem-solving or learning that employs a method not guaranteed to be optimal or perfect, but sufficient for reaching an immediate goal. Heuristics are used to speed up the process of finding a satisfactory solution, where an exhaustive search is impractical. This approach is often employed in [[artificial intelligence]], [[computer science]], and [[cognitive psychology]].
A '''heuristic''' is a practical approach to problem-solving or learning that employs a method not guaranteed to be optimal or perfect, but sufficient for reaching an immediate goal. Heuristics are used to speed up the process of finding a satisfactory solution, where an exhaustive search is impractical. This approach is often employed in [[artificial intelligence]], [[computer science]], and [[cognitive psychology]].


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==References==
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[[Category:Problem solving]]
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[[Category:Artificial intelligence]]
[[Category:Artificial intelligence]]
[[Category:Cognitive psychology]]
[[Category:Cognitive psychology]]

Latest revision as of 15:25, 10 November 2024

An approach to problem-solving or learning that employs a practical method not guaranteed to be optimal or perfect, but sufficient for reaching an immediate goal.


A heuristic is a practical approach to problem-solving or learning that employs a method not guaranteed to be optimal or perfect, but sufficient for reaching an immediate goal. Heuristics are used to speed up the process of finding a satisfactory solution, where an exhaustive search is impractical. This approach is often employed in artificial intelligence, computer science, and cognitive psychology.

Overview[edit]

Heuristics are strategies derived from previous experiences with similar problems. These strategies can be mental shortcuts that ease the cognitive load of making a decision. Examples of heuristics include using a "rule of thumb", an educated guess, an intuitive judgment, or common sense.

In the context of artificial intelligence, heuristics are used to improve the efficiency of algorithms. For example, in search algorithms, a heuristic function is used to rank alternatives at each branching step based on available information to decide which branch to follow.

Types of Heuristics[edit]

There are several types of heuristics, including:

  • Availability heuristic: This involves making a decision based on the information that is most readily available, rather than all possible information.
  • Representativeness heuristic: This involves judging the probability of an event by comparing it to an existing prototype in our minds.
  • Anchoring and adjustment heuristic: This involves relying heavily on the first piece of information encountered (the "anchor") when making decisions.

Applications[edit]

Heuristics are widely used in various fields:

  • In medicine, heuristics can help doctors make quick decisions in emergency situations by relying on their experience and intuition.
  • In computer science, heuristics are used in algorithms to find approximate solutions to complex problems, such as in machine learning and data mining.
  • In psychology, heuristics are studied to understand how people make decisions and solve problems.

Criticism[edit]

While heuristics can be useful, they can also lead to cognitive biases and errors in judgment. For example, the availability heuristic can lead to overestimating the likelihood of events that are more memorable or recent.

Also see[edit]

References[edit]

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