Search Algorithm
Systematically exploring possibilities to find a good one — AI's oldest technique, its most complete success, and nobody calls it AI anymore.
Reviewed July 13, 2026Stable
When not to use it
- When you don't have a model of the problem. Search needs rules. If you only have examples, learn.
- On exponential spaces without a good heuristic. Branching^depth doesn't yield to hardware.
- With an inadmissible heuristic, expecting optimality. Overestimate and A*'s guarantee is gone.
- When good-enough is fine and the space is huge. Local search is the right tool.
Reach for something else instead
- Learning — when you have examples and no model.
- MCTS with learned heuristics — when the space is too big and you have data. The AlphaGo answer.
- Constraint solvers — for scheduling and allocation, extraordinary and underused.
- Local search / annealing — huge spaces, approximate answers.
Further reading
- Hart, Nilsson & Raphael (1968), A Formal Basis for the Heuristic Determination of Minimum Cost Paths — A*, and the optimality proof.
- Campbell, Hoane & Hsu (2002), Deep Blue — search plus hand-tuned evaluation; a hardware triumph that taught the field little.
- Silver et al. (2016), Mastering the game of Go with deep neural networks and tree search — MCTS with learned heuristics. Both halves needed.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Reaching for ML when you have a model of the problem. Search is cheaper, exact and explainable.
- Expecting hardware to beat exponential branching. It doesn't. That's Lighthill's point.
- Reading Deep Blue as an AI achievement. It was search plus a hand-tuned evaluation on custom chips, and it taught the field very little.
- Missing that AlphaZero kept the search and threw out the hand-authored knowledge. That's the Bitter Lesson precisely.
At a glance
FieldFoundations
AI's oldest technique, and it won
Why nobody calls it AIthe AI effect; once it works reliably it's just software
Where the intelligence livesthe heuristic
A*'s guaranteeoptimal, if the heuristic never overestimates
The renaissancetest-time compute is search returning as "thinking"
DifficultyBeginner
Often compared with
Search vs. learning — one needs a model of the problem and gives you guarantees; the other needs examples and gives you a heuristic. AlphaGo needed both, and reasoning models are rediscovering that.
Where this sits
A destination. 1 concept lead here, and nothing in the corpus depends on it.
1Levelsteps in
1Needs firstconcepts
0Opens upnothing further
1Areastays here
Learn these firstArtificial Intelligence
Computed from the prerequisite graph, not assigned. How this works