computer science

Late move reductions

Late move reductions is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Late move reductions rather than just read about it. In short: Late Move Reductions (abbreviated as LMR) is a non-game specific enhancement to the alpha-beta algorithm and its other variants which attempts to examine a game search tree more efficiently by "pruning" bad nodes. It relies on the assumption that good game-specific move ordering causes a program to search the most likely (good) moves early.

Key takeaways

Late move reductions belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Late move reductions to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Late move reductions from memory before moving on to harder problems.

Reference excerpt

Late Move Reductions (abbreviated as LMR) is a non-game specific enhancement to the alpha-beta algorithm and its other variants which attempts to examine a game search tree more efficiently by "pruning" bad nodes. It relies on the assumption that good game-specific move ordering causes a program to search the most likely (good) moves early. If a cut-off is going to happen in a search, the first few moves are the ones most likely to cause them. In games like chess, most programs search winning captures and "killer moves" first. Late move reductions will reduce the search depth for moves searched later at a given node. This heuristic allows the program to search deeper along the critical lines, and play better. Most of the chess programs (or engines) typically search the first one or two moves in full depth. If the score of the first few moves are lower than alpha, the move is assumed bad. However, if the score of the moves are larger than alpha, the reduced search tells us nothing so we will have to do a full search (called as a fail-low). This search reduction can lead to a different search space than the pure alpha–beta method which can give different results. Care must be taken to select the reduction criteria or the search will miss some deep threats.

Description Late move reductions work on the idea that the higher a move is on a sorted list, the better it likely is. When an engine evaluates a node, it uses heuristics to order moves so that the most promising lines, such as captures or those suggested by the killer heuristic, are searched first. If these moves do not produce a cut-off, later moves are considered increasingly likely to be worse; as a result, the algorithm searches these "late" moves at a shallower depth than originally intended.

See also NNUE - A neural network which is used to evaluate moves (or calculate the approximate "score" of a move). Stockfish (chess) - A popular strong chess engine that uses NNUE as its evaluation function. YaneuraOu - A shogi chess engine that first implemented NNUE.

References

External links An Introduction to Late Move Reductions

Worked examples

Example 1 — a first encounter with Late move reductions

Start with the simplest possible case. Write down what Late move reductions claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Late move reductions before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Late move reductions ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Late move reductions

In research: Late move reductions appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Late move reductions in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Late move reductions is common in secondary-school and first-year university syllabi. It links to neighbouring topics Algorithms and data structures stubs, Computer chess, Game stubs, so understanding it makes those chapters shorter.
In everyday life: Look for Late move reductions outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Late move reductions in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Late move reductions means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Late move reductions out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Late move reductions in simple terms?

Why does Late move reductions matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Late move reductions?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Late move reductions.