GAME AI / SEARCH
CHECKERS AI
Deeper search, with a useful answer at every completed depth.
01 / THE PROBLEM
Iterative search over a changing board.
A checkers player must compare sequences of captures and replies while keeping move choice useful as the search explores farther ahead.
02 / THE APPROACH
How the system fits together.
The Java project implements a minimax player with a configured search depth, alpha-beta bounds and a board-evaluation heuristic. Its move-selection method iterates through shallower depths before returning the deepest completed result.
Checkers board → legal move generation
Successive depth limits → iterative deepening
Alternating turns → minimax values
Alpha / beta bounds → branch pruning
Board position → custom heuristic
03 / ENGINEERING DECISIONS
Keep a valid choice as search deepens
Iterative deepening searches successive depth limits and returns the best move from the last completed iteration.
Bound the branching search
Alpha-beta pruning avoids evaluating continuations that cannot change the current minimax decision.
CURRENT SCOPE
Pinned academic source with a Java board and player implementation. Individual contribution boundaries and playing strength have not been independently measured; no runtime benchmark is claimed. PAU.OS does not copy the supplied course engine or game assets.