ix-search

name	ix-search
description	Search and pathfinding with A*, Q*, MCTS, minimax, BFS/DFS
disable-model-invocation	true

Search

Find optimal paths, solve games, or explore state spaces.

When to Use

When the user needs pathfinding, game AI, decision making under uncertainty, or combinatorial search.

Algorithm Selection

Problem	Algorithm	When
Shortest path with heuristic	A*	Known goal, admissible heuristic available
Learned heuristic search	Q*	When you can train a Q-function on the domain
Game tree (deterministic)	Minimax / Alpha-Beta	Two-player zero-sum games
Game tree (stochastic)	MCTS	Large branching factor, simulation possible
Unweighted shortest path	BFS	All edges cost 1
Exhaustive exploration	DFS / IDDFS	Memory-constrained, solution depth unknown
Local optimization	Hill Climbing / Tabu	Single solution improvement

Programmatic Usage

use ix_search::astar::{SearchState, astar};
use ix_search::qstar::{QFunction, TabularQ, qstar_search};
use ix_search::mcts::{MctsState, mcts_search};
use ix_search::adversarial::{GameState, alpha_beta};

// Implement SearchState for your domain, then:
let result = astar(start_state, |s| s.manhattan_distance());

Q* vs A*

Q* uses a learned heuristic (DQN-style) instead of hand-crafted. Dramatically fewer node expansions in large action spaces. Use compare_qstar_vs_astar() to benchmark.