3 个仓库
Awesome GitHub RepositoriesSearch Pruning
Algorithms that discard branches of a game tree that cannot influence the final decision to minimize computation.
Distinct from Model Pruning: Distinct from Model Pruning: focuses on search tree pruning in game engines rather than neural network parameter reduction.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Search Pruning. Refine with filters or upvote what's useful.
Awesome Search Pruning GitHub Repositories
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- official-stockfish/stockfish
official-stockfish/Stockfish
14,802在 GitHub 上查看Stockfish is a high-performance chess engine designed to evaluate board positions and calculate optimal moves. It functions as a command-line tool that utilizes neural network-based search algorithms to assess complex game states and determine strategic advantages. The engine is fully compliant with the Universal Chess Interface, allowing it to exchange commands and move data with external graphical user interfaces and professional analysis software. The engine distinguishes itself through advanced computational strategies that maximize hardware efficiency and search depth. It employs multi-t
Implements alpha-beta pruning to optimize search depth and computational efficiency during move evaluation.
C++chesschess-enginecpp - optuna/optuna
optuna/optuna
14,388在 GitHub 上查看Optuna is a Python-based hyperparameter optimization framework designed to automate the search for optimal machine learning model configurations. It functions as a Bayesian optimization library that systematically tests parameter combinations to maximize or minimize objective functions, streamlining the model development process through iterative evaluation. The project distinguishes itself through a define-by-run dynamic construction model, which allows users to build complex, conditional search spaces using standard programming logic. Its architecture is highly modular, featuring a pluggabl
Provides automated early termination of underperforming trials to save computational resources during hyperparameter search.
Pythondistributedhyperparameter-optimizationmachine-learning - norvig/paip-lisp
norvig/paip-lisp
7,465在 GitHub 上查看This project is a comprehensive Lisp AI implementation library that provides reference implementations for various artificial intelligence paradigms and symbolic algorithms. It functions as a multi-purpose toolkit containing a logic programming engine, a natural language processing suite, and a symbolic mathematics toolkit. The library is distinguished by its diverse architectural frameworks, including a Prolog-style execution engine that uses unification and goal-driven backtracking, and a system for simulating human decision-making through expert system shells and certainty factors. It also
Implements search tree pruning to discard irrelevant branches in game-state evaluations.
Common Lisp
