Exemplo n.º 1
0
        /// <summary>
        /// public wrapper for recursion
        /// try each possible move, find the one with the best score
        /// </summary>
        /// <param name="lookahead">how far to look ahead</param>
        /// <param name="playerX">is this for player x</param>
        /// <returns>The data on the best move location and score</returns>
        public MinimaxResult DoMinimax(int lookahead, bool playerX)
        {
            // set up inital state
            DateTime startTime = DateTime.Now;

            if (lookahead < 1)
            {
                throw new Exception("Invalid lookahead of " + lookahead);
            }

            this.debugDataItems.Clear();

            Occupied player = playerX.ToPlayer();
            int      alpha  = MoveScoreConverter.ConvertWin(player.Opponent(), 0);
            int      beta   = MoveScoreConverter.ConvertWin(player, 0);

            MinimaxResult bestMove = this.ScoreBoard(lookahead, this.ActualBoard, playerX, alpha, beta);

            if (bestMove.Move != Location.Null)
            {
                GoodMoves.AddGoodMove(0, bestMove.Move);
            }

            DateTime endTime = DateTime.Now;

            this.MoveTime = endTime - startTime;

            return(bestMove);
        }
Exemplo n.º 2
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        public Minimax(HexBoard board, GoodMoves goodMoves, ICandidateMoves candidateMovesFinder)
        {
            this.actualBoard = board;
            this.goodMoves = goodMoves;
            this.candidateMovesFinder = candidateMovesFinder;

            this.boardCache = new BoardCache(board.Size);
            this.pathLengthFactory = new PathLengthAStarFactory();
        }
Exemplo n.º 3
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        public Minimax(HexBoard board, GoodMoves goodMoves, ICandidateMoves candidateMovesFinder)
        {
            this.actualBoard          = board;
            this.goodMoves            = goodMoves;
            this.candidateMovesFinder = candidateMovesFinder;

            this.boardCache        = new BoardCache(board.Size);
            this.pathLengthFactory = new PathLengthAStarFactory();
        }
Exemplo n.º 4
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        /// <summary>
        /// Initializes a new instance of the HexGame class, with a board size
        /// </summary>
        /// <param name="boardSize">size of the board</param>
        public HexGame(int boardSize)
        {
            this.board = new HexBoard(boardSize);
            IPathLengthFactory pathLengthFactory = new PathLengthAStarFactory();

            this.xPathLength = pathLengthFactory.CreatePathLength(this.board);
            this.yPathLength = pathLengthFactory.CreatePathLength(this.board);
            this.goodMoves = new GoodMoves();
            this.goodMoves.DefaultGoodMoves(boardSize, 5);
        }
Exemplo n.º 5
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        public Minimax(HexBoard board, GoodMoves goodMoves, ICandidateMoves candidateMovesFinder)
        {
            this.board = board;

            this.depth = 0;

            this.candidateMovesFinder = candidateMovesFinder;
            this.pathLengthFactory    = new PathLengthAStarFactory();
            GoodMoves = goodMoves;
        }
        public void CountOneMoveTest()
        {
            GoodMoves goods = new GoodMoves();
            CandidateMovesSelective moveFinder = new CandidateMovesSelective(goods, 0);
            HexBoard testBoard = new HexBoard(BoardSize);

            testBoard.PlayMove(5, 5, true);

            IEnumerable<Location> moves = moveFinder.CandidateMoves(testBoard, 0);

            Assert.Greater(BoardCellCount - 1, moves.Count());
        }
Exemplo n.º 7
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        public MinimaxResult DoMinimax(int depth, bool isComputer)
        {
            // megmondja, hogy ki a jatekos, ha a gep, akkor az isComputerben true van, es akkor a plyaerben xPlayer lesz.
            Occupied player = isComputer.ToPlayer();

            // a gep az alpha
            this.alpha = MoveScoreConverter.ConvertWin(player.Opponent(), 0);
            // ember a beta
            this.beta = MoveScoreConverter.ConvertWin(player, 0);
            // a minimax algoritmus magaban
            MinimaxResult bestMove = this.MiniMaxAlg(depth, isComputer, board);

            if (bestMove.Move != Location.Null)
            {
                // killer heurisztika
                GoodMoves.AddGoodMove(0, bestMove.Move);
            }
            return(bestMove);
        }
Exemplo n.º 8
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 public CandidateMovesSelective(GoodMoves goodMoves, int cellsPlayedCount)
 {
     this.goodMoves = goodMoves;
     this.cellsPlayedCount = cellsPlayedCount;
 }
Exemplo n.º 9
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        private static Minimax MakeMinimaxForBoard(HexBoard board)
        {
            GoodMoves goodMoves = new GoodMoves();
            ICandidateMoves candidateMovesFinder = new CandidateMovesAll();

            Minimax result = new Minimax(board, goodMoves, candidateMovesFinder);
            result.GenerateDebugData = true;

            return result;
        }
 public void TestCreate()
 {
     GoodMoves goods = new GoodMoves();
     CandidateMovesSelective moveFinder = new CandidateMovesSelective(goods, 0);
     Assert.IsNotNull(moveFinder);
 }
Exemplo n.º 11
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        private MinimaxResult MiniMaxAlg(int depth, bool isComputer, HexBoard board)
        {
            BoardCache    boardCache = new BoardCache(board.Size);
            MinimaxResult bestResult = null;
            Location      cutOffMove = Location.Null;
            // az ures cellakat tartalmazza
            var possibleMoves = candidateMovesFinder.CandidateMoves(board, depth);

            foreach (Location move in possibleMoves)
            {
                if (!move.IsNull())
                {
                    // nem az eredetit modositom meg, hanem letrehozok egyet a peldajara
                    HexBoard board1 = new HexBoard(board.Size);
                    board1.CopyStateFrom(board);
                    board1.PlayMove(move, isComputer);


                    // itt szamolja ki az allast
                    PathLengthBase staticAnalysis = this.pathLengthFactory.CreatePathLength(board1);
                    int            situationScore = staticAnalysis.SituationScore();
                    MinimaxResult  moveScore      = new MinimaxResult(situationScore);

                    if (depth <= 1 || MoveScoreConverter.IsWin(situationScore))
                    {
                        moveScore = new MinimaxResult(situationScore);
                    }
                    else
                    {
                        if (depth > 1)
                        {
                            // rekurzio
                            moveScore = MiniMaxAlg(depth--, !isComputer, board1);
                            moveScore.MoveWins();
                        }
                    }

                    moveScore.Move = move;
                    // Itt nezem meg, hogy a minimum kell nekunk, vagy a maximum
                    if (bestResult == null || MoveScoreConverter.MinOrMax(moveScore.Score, bestResult.Score, isComputer))
                    {
                        bestResult = new MinimaxResult(move, moveScore);
                    }

                    alpha = CheckAlpha(moveScore.Score, isComputer);
                    if (IsAlphaBetaCutoff(isComputer))
                    {
                        cutOffMove       = move;
                        bestResult.Score = alpha;
                        break;
                    }
                }
                else
                {
                    break;
                }
            }
            if (bestResult != null)
            {
                GoodMoves.AddGoodMove(depth, bestResult.Move);
            }

            if (cutOffMove != Location.Null)
            {
                GoodMoves.AddGoodMove(depth, cutOffMove);
            }

            return(bestResult);
        }
Exemplo n.º 12
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 public void TearDown()
 {
     this.goodMoves = null;
 }
Exemplo n.º 13
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 public void SetUp()
 {
     this.goodMoves = new GoodMoves();
 }
Exemplo n.º 14
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        /// <summary>
        /// private recursive worker - does the minimax algorithm
        /// </summary>
        /// <param name="lookahead">the current ply, counts down to zero</param>
        /// <param name="stateBoard">the current board</param>
        /// <param name="isPlayerX">player X or player Y</param>
        /// <param name="alpha">alpha value used in alpha-beta pruning</param>
        /// <param name="beta">beta value used in alpha-beta pruning</param>
        /// <returns>the score of the board and best move location</returns>
        private MinimaxResult ScoreBoard(
            int lookahead,
            HexBoard stateBoard,
            bool isPlayerX,
            int alpha,
            int beta)
        {
            this.CountBoards++;

            MinimaxResult bestResult = null;
            Location      cutoffMove = Location.Null;

            var possibleMoves = this.candidateMovesFinder.CandidateMoves(stateBoard, lookahead);

            foreach (Location move in possibleMoves)
            {
                // end on null loc
                if (move.IsNull())
                {
                    break;
                }

                if (this.GenerateDebugData)
                {
                    this.AddDebugDataItem(lookahead, move, isPlayerX, alpha, beta);
                }

                // make a speculative board, like the current, but with this cell played
                HexBoard testBoard = this.boardCache.GetBoard();
                testBoard.CopyStateFrom(stateBoard);

                testBoard.PlayMove(move, isPlayerX);
                MinimaxResult moveScore;

                PathLengthBase staticAnalysis = this.pathLengthFactory.CreatePathLength(testBoard);
                int            situationScore = staticAnalysis.SituationScore();

                if (lookahead <= 1)
                {
                    // we have reached the limits of lookahead - return the situation score
                    moveScore = new MinimaxResult(situationScore);
                }
                else if (MoveScoreConverter.IsWin(situationScore))
                {
                    // stop - someone has won
                    moveScore = new MinimaxResult(situationScore);
                }
                else
                {
                    // recurse
                    moveScore = this.ScoreBoard(lookahead - 1, testBoard, !isPlayerX, beta, alpha);
                    moveScore.MoveWins();
                }

                moveScore.Move = move;

                this.boardCache.Release(testBoard);

                // higher scores are good for player x, lower scores for player y
                if (bestResult == null || MoveScoreConverter.IsBetterFor(moveScore.Score, bestResult.Score, isPlayerX))
                {
                    bestResult = new MinimaxResult(move, moveScore);
                }

                // do the alpha-beta pruning
                alpha = CheckAlpha(alpha, moveScore.Score, isPlayerX);

                if (IsAlphaBetaCutoff(isPlayerX, alpha, beta))
                {
                    cutoffMove       = move;
                    bestResult.Score = alpha;
                    break;
                }

                // end a-b pruning
            }

            if (bestResult != null)
            {
                GoodMoves.AddGoodMove(lookahead, bestResult.Move);
            }

            if (cutoffMove != Location.Null)
            {
                GoodMoves.AddGoodMove(lookahead, cutoffMove);
            }

            return(bestResult);
        }
Exemplo n.º 15
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 public void TestCreate()
 {
     GoodMoves goods = new GoodMoves();
     CandidateMovesAllSorted moves = new CandidateMovesAllSorted(goods, 0);
     Assert.IsNotNull(moves);
 }
Exemplo n.º 16
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 public CandidateMovesAllSorted(GoodMoves goodMoves, int cellsPlayedCount)
 {
     this.goodMoves = goodMoves;
     this.cellsPlayedCount = cellsPlayedCount;
 }