Board Game Engine Research: A Multilingual Reading List for Chess, Checkers and Backgammon

Updated October 2026 · 291 articles · 16 languages

Three games have shaped how computers learn to play. Chess pushed search to its limits and then handed evaluation to neural networks. Checkers became the largest game ever solved outright, when Jonathan Schaeffer's Chinook team proved in 2007 that perfect play ends in a draw. Backgammon added dice, which forced engines to reason about probability instead of certainty, and gave us TD-Gammon, one of the first programs to teach itself through reinforcement learning.

Over the past months we have written about all three games for readers in many languages. This page gathers every one of those articles in one place, sorted by game and then by language, so you can follow one idea, such as bitboards or bear-off probabilities, across languages, or read the whole series in your own.

How the three games differ

Chess has around 1043 legal positions, far too many to solve, so engines combine fast move generation with deep alpha-beta search and a learned evaluation. Modern NNUE networks update incrementally as pieces move, which is why they run fast enough on an ordinary CPU.

Checkers has about 5×1020 positions. That is small enough that a mix of forward search and backward analysis from the endgame could close the gap. The whole board fits in a handful of 32-bit integers, and the mandatory-capture rule prunes huge parts of the tree.

Backgammon is a game of chance with full information. Each turn has 21 distinct dice outcomes, so search must average over them. Late in the game, when the two sides no longer interact, the race reduces to an absorbing Markov chain that can be solved exactly. The doubling cube then turns every position into a pricing problem much like a financial option.

A note on fair dice. Online backgammon players have long suspected that dice are rigged. The cryptographic answer is a commit-reveal scheme: the server commits to a secret seed before the game by publishing its hash, the player adds their own randomness, and once the game ends the seed is revealed so anyone can recompute every roll. Boardgammon publishes how its implementation works on its provably fair backgammon dice page, and several articles below walk through the HMAC-SHA256 construction step by step.

Chess engines and chess learning 60

Bitboards, magic move generation, alpha-beta search, quiescence search, NNUE evaluation and server-authoritative online play.

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Checkers and draughts 81

32-bit board representations, mandatory-capture move generation, endgame databases and the proof that 8x8 checkers is a draw.

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Backgammon, probability and fair dice 105

Expectiminimax search, bear-off Markov chains, doubling-cube economics and commit-reveal dice that players can verify.

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Game theory, history and engine engineering 45

State-space complexity, the history of race and capture games, and the networking side of real-time board games.

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Try the ideas yourself

Reading about search trees is one thing; watching an engine find a move is another. All three games are free to play in the browser on Boardgammon. On each one you can play against engines of increasing strength and review your games afterwards: