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Heuristic

As explained in Inside AI

A heuristic is a practical rule or approximation for making decisions when exhaustive computation is impractical.

A heuristic is a practical rule or approximation used when exhaustive computation is impractical. In turn-based games, it helps a program decide which possibilities deserve deeper analysis and when to make a move. section 7.1

Chess search

A chess program searches a game tree, prunes less promising branches, and evaluates resulting positions. Heuristic rules estimate advantages for each side, convert them into weighted numerical scores, and combine them into an overall position evaluation. The program must balance spending time to find a stronger move against preserving time for later turns. section 7.1

Heuristics make large search problems manageable, but they rely on approximations rather than complete calculation. Their usefulness therefore depends on choosing sensible evaluation rules and managing limited computation without rushing into poor decisions. section 7.1

How Inside AI covers it

How deep it goes
Explains heuristic scoring and time allocation within chess search, while stopping short of formal derivation or implementation detail.
Examples
Uses chess programs, Deep Blue, weighted position scores, opening databases, and endgame databases as concrete examples.
What you can do after
Evaluate how approximate rules can make large game searches manageable under strict time limits.
How it connects
Connects heuristics with game trees, pruning, alpha-beta pruning, position evaluation, and game databases.

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This explainer was generated by Manning's knowledge graph from the text of Inside AI. The section links point to the parts of the book it draws on.