ai working and winning, added move rank heuristic
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5 changed files with 169 additions and 31 deletions
15
README.md
15
README.md
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@ -1,13 +1,11 @@
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# Othello
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## Project Structure
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## Running
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```
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├── iagorithms <-- contains expert system AI library
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│ └── ...
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├── othello <-- contains main binary and library for Othello
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│ └── ...
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└── README.md <-- you are here
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I recommend using the release profile for the compiler optimizations. These are
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quite important since our algorithms involve evaluating millions of moves.
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```sh
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cargo run --release
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```
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## Game Representation
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@ -15,6 +13,3 @@
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The game state is represented by what's known as a
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[BitBoard](https://www.chessprogramming.org/Bitboards). I knew about these
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already from researching Chess programming in the past.
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This does mean that when calling upon iagorithms' it will need to _explode_
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the bitboard into a format that can be pattern matched on.
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135
src/ai.rs
135
src/ai.rs
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@ -1,33 +1,119 @@
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use crate::{
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board::{Board, explode_board},
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board::{Board, explode_board, squares::*},
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game::{Game, Team},
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};
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/// Contains all corner squares
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const CORNERS: Board = A1 | A8 | H1 | H8;
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/// Contains all edge squares
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const EDGES: Board = A2
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| A3
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| A4
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| A5
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| A6
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| A7
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| B1
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| B8
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| C1
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| C8
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| D1
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| D8
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| E1
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| E8
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| F1
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| F8
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| G1
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| G8
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| H2
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| H3
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| H4
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| H5
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| H6
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| H7;
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#[derive(PartialEq, Eq, PartialOrd, Ord)]
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/// Represents the _value_ of a move. Some moves at face value
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/// better than others.
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enum MoveRank {
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Corner(Board),
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Edge(Board),
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Other(Board),
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}
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impl From<Board> for MoveRank {
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fn from(value: Board) -> Self {
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// Do bitwise operations to check if we have a
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// corner or edge move.
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if value & CORNERS > 0 {
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Self::Corner(value)
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} else if value & EDGES > 0 {
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Self::Edge(value)
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} else {
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Self::Other(value)
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}
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}
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}
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impl MoveRank {
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/// Unwrap underlying move value out of rank structure
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fn into_inner(self) -> Board {
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match self {
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Self::Corner(m) => m,
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Self::Edge(m) => m,
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Self::Other(m) => m,
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}
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}
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}
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/// Using alpha-beta pruning and the minimax algorithm, determine the best move
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/// for a game with a recursion depth of `depth`.
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pub fn alphabeta(game: Game, depth: u8, mut alpha: i8, mut beta: i8) -> (Board, i8) {
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///
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/// We use a very simple evaluation heuristic: (Black squares - White squares).
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pub fn alphabeta(mut game: Game, depth: u8, mut alpha: i8, mut beta: i8) -> (Board, i8) {
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// if we reach our maximum recursion depth, return evaluation
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if depth == 0 {
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return (0, game.score().diff());
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}
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let moves = game.available();
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if moves == 0 {
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return (0, game.score().diff());
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// if no move, skip and continue recursion
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// this seems to technically introduce a bias against move-chains
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// that include skips. I haven't found it to be a big deal in play.
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game.skip();
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return (0, alphabeta(game, depth - 1, alpha, beta).1);
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}
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// just initially assume that the best move is no move at all. This will
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// inevitably be corrected.
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let mut best_move: Board = 0;
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// we initially rank moves based on a couple basic heuristics:
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// - corner pieces are best
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// - edge pieces are great
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// - others considered last
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// This just allows us to prune the tree a bit more aggressively
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// since we're considering the "best" moves first.
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// We do this by mapping moves to ranked moves and then sorting.
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let mut moves = explode_board(moves).map(MoveRank::from).collect::<Vec<_>>();
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moves.sort();
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let moves = moves
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.into_iter()
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.map(MoveRank::into_inner)
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.collect::<Vec<_>>();
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// I just establish a convention of maximizing for black and minimizing for white.
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// I'm not sure if that's conventional or not, but it's what I chose.
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match game.current_team {
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Team::Black => {
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for mv in explode_board(moves) {
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for mv in moves {
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let mut g = game.clone();
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g.play(mv);
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// maximize for the evaluation of subsequent moves
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let evaluation = alphabeta(g, depth - 1, alpha, beta).1;
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// if our evaluated move is superior to the alpha, update
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// it.
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if evaluation > alpha {
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alpha = evaluation;
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best_move = mv;
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};
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// if our beta is less than alpha, prune the node.
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if beta <= alpha {
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break;
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}
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@ -35,15 +121,18 @@ pub fn alphabeta(game: Game, depth: u8, mut alpha: i8, mut beta: i8) -> (Board,
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(best_move, alpha)
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}
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Team::White => {
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for mv in explode_board(moves) {
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for mv in moves {
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let mut g = game.clone();
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g.play(mv);
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// maximize for the evaluation of subsequent moves
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// minimize for the evaluation of subsequent moves
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let evaluation = alphabeta(g, depth - 1, alpha, beta).1;
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// if our evaluated move produces lower eval than the beta,
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// update beta.
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if evaluation < beta {
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beta = evaluation;
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best_move = mv;
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};
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// if our beta is less than alpha, prune the node.
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if beta <= alpha {
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break;
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}
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@ -56,8 +145,8 @@ pub fn alphabeta(game: Game, depth: u8, mut alpha: i8, mut beta: i8) -> (Board,
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::board::BitBoard;
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use crate::board::view::View;
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use crate::board::{BitBoard, squares::*};
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use crate::game::Game;
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#[test]
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@ -78,4 +167,36 @@ mod tests {
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println!("{}", game.board().render(View::RankAsc, vec![]));
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assert_eq!(best_move, C3);
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}
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// I found that, despite the AI clobbering me, the AI could not
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// compete with itself very well. I'm honestly not quite sure why that is.
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#[test]
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#[should_panic] // disabled until I fix whatever causes the AI not to tie
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fn ai_ties_ai() {
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// just play through a game letting AI make all the moves.
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let mut game = Game::default();
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while !game.is_complete() {
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if game.available() == 0 {
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game.skip();
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} else {
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let (mv, _) = alphabeta(game.clone(), 8, i8::MIN + 1, i8::MAX - 1);
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game.play(mv);
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}
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}
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// one would assume the AI would compete rather closely against itself.
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assert!(dbg!(game.score()).diff().abs() < 3);
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}
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#[test]
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fn move_ordering() {
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let mv = A1 | A8 | C3 | D5 | A4;
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let mut moves = explode_board(mv).map(MoveRank::from).collect::<Vec<_>>();
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moves.sort();
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let moves = moves
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.into_iter()
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.map(MoveRank::into_inner)
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.collect::<Vec<_>>();
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assert_eq!(moves, vec![A1, A8, A4, C3, D5]);
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}
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}
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@ -18,6 +18,7 @@ enum Action {
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Ai,
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}
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/// Regex to match on valid play expressions. The file and rank are captured.
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const PLAY_RE: &str = r"^(play - )?([abcdefghABCDEFGH])(\d)$";
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pub fn run() -> anyhow::Result<()> {
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@ -26,8 +27,11 @@ pub fn run() -> anyhow::Result<()> {
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let play_re = Regex::new(PLAY_RE).unwrap();
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// loop until game is complete
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while !game.is_complete() {
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// compute legal moves
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let legal_moves = game.available();
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// print the board only if we're in a state where we need to
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if board_changed {
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let Score(b, w) = game.score();
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println!("Score: (Black: {} , White: {}", b, w);
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@ -41,13 +45,16 @@ pub fn run() -> anyhow::Result<()> {
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board_changed = false;
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}
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// in rust, a loop can return a value via `break`
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// loop until the user submits a valid choice
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let choice = loop {
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println!("Please choose your action: [play - (move), skip, ai]");
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let mut raw_input = String::new();
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io::stdin()
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.read_line(&mut raw_input)
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.context("Failed to read input")?;
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if let Some(captures) = play_re.captures(&raw_input.trim()) {
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// pattern match on optional regex match for play.
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if let Some(captures) = play_re.captures(raw_input.trim()) {
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let file = captures.get(2).context("Failed to get file capture")?;
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let rank = captures
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.get(3)
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@ -58,6 +65,7 @@ pub fn run() -> anyhow::Result<()> {
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break Action::Play(create_move(file.as_str(), rank));
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}
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// match raw strings for other options
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match raw_input.as_str().trim() {
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"skip" => break Action::Skip,
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"ai" => break Action::Ai,
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}
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};
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// apply user action by pattern matching
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match choice {
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Action::Play(mv) => {
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// if move is legal, apply move and re-render
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if mv & legal_moves == 0 {
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println!(
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"Attempted illegal moves. Legal moves are indicated by asterisks (*)."
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}
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}
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Action::Skip => {
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// only skip if the player has no legal moves
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if legal_moves != 0 {
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println!("Cannot skip with legal moves available. Must choose `play` or `ai`.");
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} else {
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board_changed = true;
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game.skip()
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}
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}
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Action::Ai => {
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let (mv, eval) = alphabeta(game.clone(), 12, i8::MIN + 1, i8::MAX - 1);
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println!("beep. boop. eval = {eval}");
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game.play(mv);
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if legal_moves == 0 {
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println!("beep. boop. no legal moves. skipping turn");
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game.skip();
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} else {
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let (mv, eval) = alphabeta(game.clone(), 14, i8::MIN + 1, i8::MAX - 1);
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println!("beep. boop. eval = {eval}");
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game.play(mv);
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}
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board_changed = true;
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}
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}
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}
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game.play(othello::board::squares::E6);
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println!();
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let end_score = game.score();
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println!(
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"{}",
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game.board().render(
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game.current_team,
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vec![Overlay(
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game.board().available(game.current_team),
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"\x1b[34m*\x1b[37m"
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)]
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)
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"Game Over!\nScore: Black {} to White {}",
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end_score.0, end_score.1
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);
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println!(
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"Game board:\n{}",
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game.board().render(View::RankAsc, Vec::new())
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);
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Ok(())
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@ -129,6 +143,8 @@ mod tests {
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use othello::board::squares::*;
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#[test]
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fn create_move_works() {
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// validate that we can create moves from
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// human-readable data
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assert_eq!(create_move("a", 1), A1);
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assert_eq!(create_move("d", 3), D3);
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assert_eq!(create_move("h", 8), H8);
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@ -136,7 +152,9 @@ mod tests {
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#[test]
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fn re_works() {
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// validate that the regex will match valid move expressions
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let play_re = Regex::new(PLAY_RE).unwrap();
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assert!(play_re.is_match("play - d3"));
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assert!(play_re.is_match("d3"));
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}
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}
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@ -67,6 +67,7 @@ impl Game {
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pub fn is_complete(&self) -> bool {
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let score = self.board.score();
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score.0 + score.1 == 64
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|| (self.board.available(Team::Black) | self.board.available(Team::White) == 0)
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}
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}
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@ -1,3 +1,6 @@
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///! Student: Jack Branch - 103-93-063
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///! Prof: Dr. Mike O'Neal
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///! Class: Artificial Intelligence
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mod cli;
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fn main() -> anyhow::Result<()> {
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