Function search 

pub fn search<S: State>(
    tt: &Rc<RefCell<TranspositionTable>>,
    sef: &impl StaticEvaluator<S>,
    rg: &impl ResponseGenerator<State = S>,
    s0: &Rc<S>,
    max_depth: i32,
) -> Option<Rc<S>>

A minimax search implementation using alpha-beta pruning and a transposition table.

This function performs a complete minimax search to find the best move for the current player. It uses alpha-beta pruning for efficiency and a transposition table to avoid redundant calculations.

Arguments

• tt - A transposition table for caching previously computed positions. Can be reused across multiple searches.
• sef - The static evaluation function
• rg - The response generator
• s0 - The state to search from
• max_depth - Maximum search depth in plies

Returns

Some(Rc<S>) containing the best move found, or None if no valid moves exist

Examples

use std::cell::RefCell;
use std::rc::Rc;
use crate::minimax::search;
use crate::transposition_table::TranspositionTable;

// Set up the search components
let transposition_table = Rc::new(RefCell::new(TranspositionTable::new(1000, 100)));
let evaluator = MyStaticEvaluator::new();
let move_generator = MyResponseGenerator::new();
let game_state = Rc::new(MyGameState::initial_position());

// Search to depth 6
match search(&transposition_table, &evaluator, &move_generator, &game_state, 6) {
    Some(best_move) => println!("Best move: {:?}", best_move),
    None => println!("No moves available"),
}

Algorithm Details

The search uses:

• Minimax: Alternating maximizing and minimizing players
• Alpha-beta pruning: Early termination of unpromising branches
• Transposition table: Caching of previously evaluated positions
• Move ordering: Better moves searched first for more effective pruning