AI / ML & Algorithms
PythonGame TheoryAlpha-Beta PruningHeuristic EvaluationML
Overview & Objective
A custom chess engine combining classical game tree search algorithms (Alpha-Beta pruning, iterative deepening, transposition tables) with machine learning-inspired position evaluation functions.
Key Engineering Highlights
High-Efficiency Move Generation
Designed bitboard-inspired board representation and move generation logic to maximize nodes evaluated per second.
Optimized Search Tree
Implemented minimax search with Alpha-Beta pruning, move ordering (MVV-LVA, killer moves), and quiescence search to avoid the horizon effect.
Evaluation Function
Blended material weighting, piece-square tables, king safety, and positional pawn structure analysis to assess complex game states.