One is deterministic and bigger for weaker levels, and one is For each move score we add two terms, both dependent on Int delta = std::min(topScore - rootMoves.score, PawnValueMg) RootMoves are already sorted by score in descending order Static PRNG rng(now()) // PRNG sequence should be non-deterministic
Idea by Heinz van Saanen.Ĭonst RootMoves& rootMoves = Threads.main()->rootMoves using a statistical rule dependent on 'level'. When playing with strength handicap, choose best move among a set of RootMoves MultiPV: 1, Skill Level: 20, Move Overhead: 10, Minimum Thinking. The algorithm finally chooses the strongest move after these score perturbations. Wraps the open-source Stockfish chess engine for easy integration into python. If there are many good moves, delta will be small, so each move receives a smaller score push. Then, each move is strengthened (has its score increased) by an amount depending on weakness and a random scaling of delta, with % weakness limiting how much effect delta has. delta is computed as the score difference between the best move, ordered first, and the kth best move, however this difference is capped at the value of a pawn to ensure it is not too large. Here's a summary: the algorithm considers the top-k moves, where k is multiPV, an option chosen by the user and set to at least 4 in the code.
STOCKFISH CHESS SKILL LEVEL FULL
If (skill.enabled() & skill.time_to_pick(rootDepth))Īnd this is the full algorithm used. If skill level is enabled and time is up, pick a sub-optimal best move
Skill.best ? skill.best : skill.pick_best(multiPV))) Std::swap(rootMoves, *std::find(rootMoves.begin(), rootMoves.end(), Relevant parts from search.cpp: // If skill level is enabled, swap best PV line with the sub-optimal one The Stockfish authors did a great job because their code is easily followable and commented. Since I could not find any complete explanation, I followed the eternal advice "Use the source, Luke".
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