AI Reference 018 Classical AI & Machine Learning Wikipedia guided Primary sources linked

Heuristics and Problem Solving

Definition

A heuristic is an estimate or rule used to guide search toward promising states without exhaustively checking every alternative.

What is a heuristic?

A heuristic is a rule or numerical estimate used to guide problem solving toward promising choices. In search algorithms, a heuristic function often estimates how far a current state is from a goal.

Why heuristics help

Many problem spaces are too large to explore exhaustively. A good heuristic reduces wasted exploration by ranking states that appear more useful. In A* search, for example, the heuristic is combined with the cost already accumulated.

What heuristics cannot guarantee

A heuristic is guidance, not proof. Some heuristics are designed with properties that preserve optimality; others trade accuracy for speed. The general lesson remains important in AI: reducing a huge search space often requires useful approximations rather than brute force.

Related terms, defined

Classical AI & Machine Learning Knowledge Representation Knowledge representation studies how facts, relations, rules and concepts can be encoded so a computer system can reason over them. Classical AI & Machine Learning Expert Systems Expert systems are narrow rule-based programs that separate a knowledge base from an inference mechanism to reproduce parts of specialist decision-making. Classical AI & Machine Learning ELIZA and Early Conversational Programs ELIZA, created by Joseph Weizenbaum in the 1960s, used pattern matching and scripted text transformations to simulate conversation without modern statistical language understanding. Classical AI & Machine Learning SHRDLU and Language in a Blocks World SHRDLU, developed by Terry Winograd around 1968–1970, accepted natural-language instructions about a small simulated blocks world and combined language parsing with a symbolic world model. Classical AI & Machine Learning The AI Winters AI winters were periods in which enthusiasm and funding fell after systems failed to meet expectations or became economically unattractive. The term is especially associated with downturns in the 1970s and again in the late 1980s and early 1990s.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

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