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fair-play

chess cheat detection

Lichess Irwin, Chess.com fair play

SystemTechniques · 10Primitives · 35 fair-play Alpha-beta searchgame-ai · medium: Minimax that skips branches which cannot change the result; with iterative deepening, PVS and aspiration windows it is the core of classical adversarial search.›Batched MCTS inferencegame-ai · medium: Feed an accelerator from tree search driven by a learned evaluator: virtual loss gathers leaves within a tree, parallel searches fill batches, an async queue overlaps CPU and GPU.›Decision quality scoringgame-ai · medium: Measuring move quality vs an engine: centipawn loss, win-probability accuracy, top-move matching ratesGradient-boosted decision trees (GB…machine-learning · medium: Add small regression trees one at a time, each fitted to the loss gradient; histogram splits over 256 bins make it the strongest tabular default.›Behavioral biometricssecurity · medium: Build a per-user profile of input timing and motion, score each new session by distance to it, and feed the result into a risk score instead of a verdict.›Hypothesis testingstatistics · medium: Pick a test statistic, compare it to its distribution under "no effect", read off a p-value, and control error rates across one test or many.›Monte Carlo tree searchgame-ai · medium: Grows a search tree one simulation at a time, picking visits by a bandit rule (UCT or PUCT) and backing up averages; the search behind AlphaZero.›Transposition tablegame-ai · low: A fixed-size, lossy hash table of search results keyed by a state hash; gives ordering and cutoffs for repeated states, shared lock-free between threads.›Zobrist hashinggame-ai · low: A state's hash is the XOR of one random 64-bit key per (item, slot) fact that holds in it, updated in a few XORs per change.›Compact state encodingdata-structures · low: Pack a game position into a few fixed bytes (24 for chess: occupancy bitboard plus nibbles) for training data and dedupeNNUE incremental evaluationgame-ai · low: Efficiently updatable neural network: a huge sparse first layer whose output (the accumulator) is patched per move, then tiny int8 layers; fast neural eval.›Elo ratinggame-networking · low: One number per competitor, moved by K × (actual − expected score) after each pairwise result; expected score is logistic in the rating difference.›Glicko-2game-networking · low: Rating plus rating deviation (RD) and volatility per player, so new or inactive players move fast and established ones move slowly.›Calibrationmachine-learning · low: Make predicted probabilities match observed frequencies: measure with ECE, fix post hoc with temperature, Platt or isotonic scaling on held-out data.›Evaluation metricsmachine-learning · low: Precision, recall, ROC AUC, average precision, log loss, NDCG and pass@k computed correctly, plus paired confidence intervals for comparing models.›Recurrent networksmachine-learning · low: GRU and LSTM cells for streaming sequence modelsConvolutional networksml-training · low: Layers that slide a small shared kernel over a grid, giving translation equivariance and few parameters; still the most compute-efficient choice for many vision tasks.›Candidate ordering and pruninggame-ai · low: The heuristics that make alpha-beta and branch-and-bound fast: try likely-best candidates first (TT, captures, killers, history) and skip or reduce the rest (null move, LMR, futility).›Search time managementgame-ai · low: Allocate thinking time per move from clock, increment and search instabilityMPMC bounded queueconcurrency · low: Vyukov's array queue for many producers and consumers: each slot carries a sequence number, so one CAS per operation claims a slot without locks.›Linear and logistic regressionmachine-learning · low: Fit a weighted sum of features to a number (least squares) or a probability (logistic); the baseline every model must beat.›Quantile sketchesdata-structures · low: Mergeable quantile sketches: t-digest, DDSketch, HDR histogramsLoss functionsmachine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.›+ 3 more techniques+ 19 more primitives

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