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C# / C++ CLI Micro Chess (Huo Chess)

, 23 Aug 2014
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An article about Huo Chess, a chess program in C++ and C# that attempts to be smaller in size than the Commodore-era Microchess

 

 

 

Huo Chess (GUI edition) 

Summary

Huo Chess is a chess program of just 55 KB in size (this size refers to the 0.961 C# console application version without GUI). Two versions currently exist: One without GUI (C# console application) and one with GUI (C# windows application). Previous versions included C++, Visual Basic and XNA-based editions (they were maintained up to 0.82 version - I intend to update them as soon as I have time).  In its current version (Huo Chess v0.95 – C#) can think up to 20 half-moves [Kakos-Minimax edition] (e.g. 10 half-moves for white and 10 half-moves for black pieces when computer plays with White) and has an opening book capability (the other editions will be shortly updated). An Opening Book Editor (written in C++) is also distributed. Huo Chess plays decent chess and has managed to draw Microchess, the first microchess from the Commodore era (see the Huo Chess Games Archive below). Its algorithm is based on brute-force analysis while utilizing the MiniMax algorithm. It can be used to study the underlying logic of a chess program or as a basis for your own chess program. The source code is heavily commented in English and easily customizable, since all variables have distinct and understandable names. The source code is continually improving and is distributed under The Code Project Open License. In this article, I also analyze its programming logic, with emphasis on the general flow inside the computer’s "mind," from the initial scanning of the chessboard up to the final decision on which move to play. Any comments are welcome! 

 

Introduction 

What is most important when you program? Is it the program design? Yes? If "yes," then why is it that every program today is bigger than 123876 GB and requires 120987 MB of RAM to run? Why is every new project and program — by default — larger in size and slower in speed than the previous version? The answer is simple: because of the increasing speed of computers, no one actually cares about optimizing the code structure and the software design, since the new processor will definitely make the program look fine for the end user. During the time of Commodore computers, memory was rather limited. That is why programmers tried to make the code small and efficient. This resulted in Microchess, which was a very efficient, small (as the name "micro," which means "small" in Greek, implies) chess program that was able to play chess in the few KB available in computers of that time.

What I Accomplished

Driven by the above, I started developing a program in CLI C++ v8.0 (with Visual Studio 2008 Express Edition, while now the latest edition is developed in Visual Studio 2012) to play chess with the intention of making a small in size (Micro)chess, even smaller than the Commodore-era Microchess. Now, one version in C# programming language and one version with Graphical User Interface also exist (you can find them here for downloading). I named it "Huo Chess" for personal reasons. The program plays decent chess, but unfortunately will probably lose if it plays with Garry Kasparov. Its size is currently 55 KB (in the C# edition v0.961), while the respective emulation of Microchess in C is 56 KB. However, it must be noted that the original Microchess, written in pure Assembly, was about 1 KB…something that I believe no one will ever match! In matches against Microchess, version 0.4 has managed to draw Microchess (see below the section of Huo Chess Games Archive).

How to Customize - Optimize the Program

The program has an opening book capability, which means that anyone can optimize the program by adding his/her own opening moves data in the respective folder Huo Chess Opening Book (which should be in the same directory with the executable) with Huo Opening Book Editor.

You can also add more thinking depth capability, by adding new ComputerMove functions (like Analyze_Move_2_ComputerMove, Analyze_Move_4_ComputerMove, etc. for each new thinking depth layer) and make the necessary adjustments to the filters applied to the moves analyzed (these are needed to increase the speed of the program – see FindAttackers / FindDefenders).

You can also optimize the way Huo Chess thinks by changing the CountScore function and the way the computer (or its human opponent) values the pieces or the chessboard position. For example, if you change the score of the Queen in the CountScore function from 9 to 12, then the HY will play aggressively to attack the opponent's queen and at the same time try harder to defend its own queen. You can also — for example — give a high scoring to the existence of an opening column with a rook controlling it, so as to make the computer play more with its rooks and try to take over columns with them.

Any FEEDBACK is WELCOME with better configurations of the Opening Book or the CountScore functions which are actually the heart of the system!

Current limitations of version 0.961 and thoughts for improvements

The current version (v0.961) is set to a thinking depth of 2. This is because the full blown MiniMax algorithm is very memory intensive and time consuming. You can set the thinking depth to 3 to make the computer think better, but when you set the depth to 4 the program crashes. This happens because of the size limits of the array which holds the scores of each node (i.e. of each possible position the computer analyzes). One thought would be to create different arrays for every level of thinking or to split the thinking into threads so that it is faster. Another way out of this is to apply filters to the algorithm so that it does not spend time and resources analyzing every posible move. All these are thoughts for the next upcoming version...

I. Chess Algorithm Analysis (version 0.961)

The program implements the MiniMax algorithm. Huo Chess plays with the material in mind, while its code has some hints of positional strategic playing embedded. More analytically: When the program starts thinking, it scans the chessboard to find where its pieces are (see ComputerMove function) and then tries all possible moves it can make. It analyzes these moves up to the thinking depth I have defined (via the ComputerMove -> Analyze_Move_1_HumanMove -> Analyze_Move_2_ComputerMove path), measures the score (see CountScore function) of the final position reached from all possible move variants and – finally – chooses the move that leads to the most promising (from a score point of view) position (ComputerMove function).

C# v0.961 Kakos-Minimax algorithm summary

A high-level example of the progress is as follows:

  1. ComputerMove: Scans the chessboard and makes all possible moves
  2. CheckMove: Stores the initial values of the move and makes some additional checks (e.g. for dangerous squares in the chessboard)
  3. (if thinking depth not reached) => call Analyze_Move_1_HumanMove
  4. Analyze_Move_1_HumanMove: Checks all possible answers of the human opponent
  5. (if thinking depth not reached) => call Analyze_Move_2_ComputerMove
  6. Analyze_Move_2_ComputerMove: Scans the chessboard and makes all possible moves for the computer at the next thinking level
  7. (if thinking depth reached) => record the score of the final position in the Nodes of the MiniMax algorithm
  8. The algorithm continues until all possible moves are scanned
  9. The MiniMax algorithm is finally used to calculate the best move via the analysis of the thinking tree Nodes

You can uncomment the log writting code (see #region WriteLog, #region NodesLogBEFORE and #region NodesLogAFTER) to see the progress of the computer thought while the computer thinks in the respective .txt files that will be created as the result of the thinking process.

Huo Chess 0.94 with huo_DEBUG set to true: you can see how computer thinks and optimize it

II. Huo Chess Thought Flow [Minimax algorithm] (version 0.961)

Below, I analyze the thought flow of the chess program. I will describe only the main steps and code segments, so as to show the way the computer scans the chessboard, conducts all possible moves and finally finds the better one. The function names appear in bold, i.e. ComputerMove - Start indicates the beginning of the ComputerMove() function. Be aware that some code segments may be slightly different from the code in the distributed ZIP file since I continuously change the program. As it appears, the "constant beta" state is the trend nowadays.

ComputerMove – Start

Initialize nodes

Store initial position

Check opening book

Check dangerous squares

Analyze all possible moves

for (...)
{

MovingPiece = Skakiera_Thinking[(iii), (jjj)];

m_StartingColumnNumber = iii + 1;
m_FinishingColumnNumber = w + 1;
m_StartingRank = jjj + 1;
m_FinishingRank = r + 1;

// Store temporary move data in local variables, so as to use them in the Undo of the move at the end of this function (MovingPiece, m_StartingColumnNumber, etc variables are changed by next functions as well, so using them leads to problems)

MovingPiece0             = MovingPiece;
m_StartingColumnNumber0  = m_StartingColumnNumber;
m_FinishingColumnNumber0 = m_FinishingColumnNumber;
m_StartingRank0          = m_StartingRank;
m_FinishingRank0         = m_FinishingRank;
ProsorinoKommati0        = Skakiera_Thinking[(m_Finishi…)];

} (check all possible moves)

Call CheckMove(Skakiera_Thinking)  to perform some checks:

  • Store initial move coordinates
  • Check for dangerous squares
  • Check for mate
  • Check for check
  • Check the legality and the validity of the move (only for ComputerMove)

For each possible move

  • Count score
  • Increase nodes count
  • Store the score at that node
  • Call next function to search at a deeper level...

If the move under analysis is correct and legal, do it and measure its score

if ((m_OrthotitaKinisis == true) && (m_NomimotitaKinisis == true))

{

DO THE MOVE

// Measure score AFTER the move

if (Move_Analyzed == 0)
{
NodeLevel_0_count++;
Temp_Score_Move_0 = CountScore(Skakie…);
}

if (Move_Analyzed == 2)
{
NodeLevel_2_count++;
Temp_Score_Move_2 = CountScore(Skakie…);
}

if (Move_Analyzed == 4)
{
NodeLevel_4_count++;
Temp_Score_Move_4 = CountScore(Skakie…);
}

if (Move_Analyzed == 6)
{
NodeLevel_6_count++;
Temp_Score_Move_6 = CountScore(Skakie…);
}

If you have not reached the thinking depth, call the next level analysis functions...

if (Move_Analyzed < Thinking_Depth)

{
Move_Analyzed = Move_Analyzed + 1;

// Check human move (to find the best possible answer of the human
// to the move currently analyzed by the HY Thought process)

Who_Is_Analyzed = "Human";

if (Move_Analyzed == 1)
    Analyze_Move_1_HumanMove(Skakiera_Move_After);
else if (Move_Analyzed == 3)
    Analyze_Move_3_HumanMove(Skakiera_Move_After);
else if (Move_Analyzed == 5)
    Analyze_Move_5_HumanMove(Skakiera_Move_After);
}

If thinking level is reached, record the nodes' scores.

if (Move_Analyzed == Thinking_Depth)                                           

{

// [MiniMax algorithm - skakos]

// Record the node in the Nodes Analysis array (to use with MiniMax algorithm)

NodesAnalysis[NodeLevel_0_count, 0, 0] = Temp_Score_Move_0;
NodesAnalysis[NodeLevel_1_count, 1, 0] = Temp_Score_Move_1_human;
NodesAnalysis[NodeLevel_2_count, 2, 0] = Temp_Score_Move_2;
NodesAnalysis[NodeLevel_3_count, 3, 0] = Temp_Score_Move_3_human;
NodesAnalysis[NodeLevel_4_count, 4, 0] = Temp_Score_Move_4;
NodesAnalysis[NodeLevel_5_count, 5, 0] = Temp_Score_Move_5_human;
NodesAnalysis[NodeLevel_6_count, 6, 0] = Temp_Score_Move_6;


// Store the prnts (number of the node of the upper level)                                     

NodesAnalysis[NodeLevel_0_count, 0, 1] = 0;
NodesAnalysis[NodeLevel_1_count, 1, 1] = NodeLevel_0_count;
NodesAnalysis[NodeLevel_2_count, 2, 1] = NodeLevel_1_count;
NodesAnalysis[NodeLevel_3_count, 3, 1] = NodeLevel_2_count;
NodesAnalysis[NodeLevel_4_count, 4, 1] = NodeLevel_3_count;
NodesAnalysis[NodeLevel_5_count, 5, 1] = NodeLevel_4_count;
sAnalysis[NodeLevel_6_count, 6, 1] = NodeLevel_5_count;

}

Undo the move

Skakiera_Thinking[(m_StartingColumnNumber0 - 1), (m_StartingRank0 - 1)] = MovingPiece0;
Skakiera_Thinking[(m_FinishingColumnNumber0 - 1), (m_FinishingRank0 - 1)] = ProsorinoKommati0;

} (end of for loop)

Find if there is mate

DO THE BEST MOVE FOUND!

[MiniMax algorithm – skakos - START]

Check Minimax algorithm http://en.wikipedia.org/wiki/Minimax for how the algorithm works!

// -----------------------------------------------------
// NodesAnalysis
// -----------------------------------------------------
// Nodes structure...
// [ccc, xxx, 0]: Score of node No. ccc at level xxx
// [ccc, xxx, 1]: Parent of node No. ccc at level xxx-1
// -----------------------------------------------------


prntNodeAnalyzed = -999;


for (counter6 = 1; counter6 <= NodeLevel_6_count; counter6++)
{

if (Int32.Parse(NodesAnalysis[counter6, 6, 1].ToString()) != prntNodeAnalyzed)
{
    prntNodeAnalyzed = Int32.Parse(NodesAnalysis[counter6, 6, 1].ToString());
    NodesAnalysis[Int32.Parse(NodesAnalysis[counter6, 6, 1].ToString()), 5, 0] = NodesAnalysis[counter6, 6, 0];

}

if (NodesAnalysis[counter6, 6, 0] <= NodesAnalysis[Int32.Parse(NodesAnalysis[counter6, 6, 1].ToString()), 5, 0])

    NodesAnalysis[Int32.Parse(NodesAnalysis[counter6, 6, 1].ToString()), 5, 0] = NodesAnalysis[counter6, 6, 0];

}


prntNodeAnalyzed = -999;


for (counter5 = 1; counter5 <= NodeLevel_5_count; counter5++)
{

if (Int32.Parse(NodesAnalysis[counter5, 5, 1].ToString()) != prntNodeAnalyzed)
{
    prtNodeAnalyzed = Int32.Parse(NodesAnalysis[counter5, 5, 1].ToString());
    NodesAnalysis[Int32.Parse(NodesAnalysis[counter5, 5, 1].ToString()), 4, 0] = NodesAnalysis[counter5, 5, 0];

}

if (NodesAnalysis[counter5, 5, 0] >= NodesAnalysis[Int32.Parse(NodesAnalysis[counter5, 5, 1].ToString()), 4, 0])
    NodesAnalysis[Int32.Parse(NodesAnalysis[counter5, 5, 1].ToString()), 4, 0] = NodesAnalysis[counter5, 5, 0];

}

...

[MiniMax algorithm – skakos - END]

REDRAW THE CHESSBOARD

// now it is the other color's turn to play

if (m_PlayerColor.CompareTo("Black") == 0)

    m_WhichColorPlays = "Black";

else if (m_PlayerColor.CompareTo("White") == 0)

    m_WhichColorPlays = "White";


// now it is the human's turn to play

m_WhoPlays = "Human";

CheckMove

  • Store initial values of the move
  • Check for mate
  • Check for check
  • Check for dangerous squares
  • Check legality and validity of the move (only for ComputerMove!)

Analyze_Move_1_HumanMove

for ... (check all possible moves)

{

// Store temporary move data in local variables, so as to use them in the Undo of the move at the end of this function (the MovingPiece, m_StartingColumnber, etc variables are changed by next functions as well, so using them leads to problems)

MovingPiece1             = MovingPiece;
m_StartingColumnNumber1  = m_StartingColumnNumber;
m_FinishingColumnNumber1 = m_FinishingColumnNumber;
m_StartingRank1          = m_StartingRank;
m_FinishingRank1         = m_FinishingRank;
ProsorinoKommati1        = Skakiera_Human_Thinking_2[(m_Fi...)];

If the move is legal and valid...

if ((m_OrthotitaKinisis == true) && (m_NomimotitaKinisis == true))

Do the move

Measure score AFTER the move

if (Move_Analyzed == 1)
{
    NodeLevel_1_count++;
    Temp_Score_Move_1_human = CountScore(Skakiera_Human_Thinking_2, humanDangerParameter);
}

if (Move_Analyzed == 3)
{
    NodeLevel_3_count++;
    Temp_Score_Move_3_human = CountScore(Skakiera_Human_Thinking_2, humanDangerParameter);
}

if (Move_Analyzed == 5)
{
    NodeLevel_5_count++;
    Temp_Score_Move_5_human = CountScore(Skakiera_Human_Thinking_2, humanDangerParameter);
}

If thinking depth not reached, call the next level thinking function...

if (Move_Analyzed < Thinking_Depth)
{
    // Call ComputerMove for the HY throught process to continue

    Move_Analyzed = Move_Analyzed + 1;
    Who_Is_Analyzed = "HY";

    for (i = 0; i <= 7; i++)
    {
        for (j = 0; j <= 7; j++)
        {
            Skakiera_Move_After[(i), (j)] = Skakiera_Human_Thinking_2[(i), (j)];
        }
    }


    if (Move_Analyzed == 2)
        Analyze_Move_2_ComputerMove(Skakiera_Move_After);
    else if (Move_Analyzed == 4)
        Analyze_Move_4_ComputerMove(Skakiera_Move_After);
    else if (Move_Analyzed == 6)
        Analyze_Move_6_ComputerMove(Skakiera_Move_After);

}

Undo the move

Return to previous level...

Move_Analyzed = Move_Analyzed - 1;

Who_Is_Analyzed = "HY";

Analyze_Move_2_ComputerMove

Analyze all possible moves

for (...)

{

 MovingPiece = Skakiera_Thinking_template[(iii), (jjj)];

 m_StartingColumnNumber = iii + 1;

 m_FinishingColumnNumber = w + 1;

 m_StartingRank = jjj + 1;

 m_FinishingRank = r + 1;

}

Check the move

  • Is it legal?
  • Is it valid?

If the move is valid and legal...

if ((m_OrthotitaKinisis == true) && (m_NomimotitaKinisis == true))

Do the move

Check the score after the computer move.

if (Move_Analyzed == 0)
{
    NodeLevel_0_count++;
    Temp_Score_Move_0 = CountScore(Skakiera_Thinking_HY_2, humanDangerParameter);
}

if (Move_Analyzed == 2)
{
    NodeLevel_2_count++;
    Temp_Score_Move_2 = CountScore(Skakiera_Thinking_HY_2, humanDangerParameter);
}

if (Move_Analyzed == 4)
{
    NodeLevel_4_count++;
    Temp_Score_Move_4 = CountScore(Skakiera_Thinking_HY_2, humanDangerParameter);
}

if (Move_Analyzed == 6)
{
    NodeLevel_6_count++;
    Temp_Score_Move_6 = CountScore(Skakiera_Thinking_HY_2, humanDangerParameter);
}

If thinking depth is reached, record the nodes in the Nodes Analysis array (to use with MiniMax algorithm) 

if (Move_Analyzed == Thinking_Depth)                                           
{

// [MiniMax algorithm - skakos]
// Record the node in the Nodes Analysis array (to use with MiniMax algorithm) skakos

NodesAnalysis[NodeLevel_0_count, 0, 0] = Temp_Score_Move_0;
NodesAnalysis[NodeLevel_1_count, 1, 0] = Temp_Score_Move_1_human;
NodesAnalysis[NodeLevel_2_count, 2, 0] = Temp_Score_Move_2;
NodesAnalysis[NodeLevel_3_count, 3, 0] = Temp_Score_Move_3_human;
NodesAnalysis[NodeLevel_4_count, 4, 0] = Temp_Score_Move_4;
NodesAnalysis[NodeLevel_5_count, 5, 0] = Temp_Score_Move_5_human;
NodesAnalysis[NodeLevel_6_count, 6, 0] = Temp_Score_Move_6;

// Store the prnts (number of the node of the upper level)

NodesAnalysis[NodeLevel_0_count, 0, 1] = 0;
NodesAnalysis[NodeLevel_1_count, 1, 1] = NodeLevel_0_count;
NodesAnalysis[NodeLevel_2_count, 2, 1] = NodeLevel_1_count;
NodesAnalysis[NodeLevel_3_count, 3, 1] = NodeLevel_2_count;
NodesAnalysis[NodeLevel_4_count, 4, 1] = NodeLevel_3_count;
NodesAnalysis[NodeLevel_5_count, 5, 1] = NodeLevel_4_count;
NodesAnalysis[NodeLevel_6_count, 6, 1] = NodeLevel_5_count;

}

If thinking depth is not reached, call the next level and so on...

if (Move_Analyzed < Thinking_Depth)
{
    Move_Analyzed = Move_Analyzed + 1;

    for (i = 0; i <= 7; i++)
    {
        for (j = 0; j <= 7; j++)
        {
            Skakiera_Move_After[(i), (j)] = Skakiera_Thinking[(i), (j)];
        }
    }

    Who_Is_Analyzed = "Human";
    First_Call_Human_Thought = true;

    // Check human move
    if (Move_Analyzed == 1)
        Analyze_Move_1_HumanMove(Skakiera_Move_After);
    else if (Move_Analyzed == 3)
        Analyze_Move_3_HumanMove(Skakiera_Move_After);
    else if (Move_Analyzed == 5)
        Analyze_Move_5_HumanMove(Skakiera_Move_After);
}

Undo the move

Return to previous level...

Move_Analyzed = Move_Analyzed - 1;

Who_Is_Analyzed = "Human";

CountScore

Every move score is measured (if the move is legal and correct). These scores are stored in the NodesAnalysis array (see below). The scoring function is the heart of the program. It currently takes into account mainly material values, with some positional considerations for the opening phase of the game (i.e. if Move < 11 it is not good to move your Queen or else a small “scoring penalty” is imposed). The optimization of that function is key to the increasing of the computer play strength.

Thinking Depth - End

When we have reached the thinking depth (i.e. when we have reached the ComputerMove function which we have defined as the last one in the chain of analysis), we store the chessboard scores of the thinking tree nodes for every thinking depth level (applies for version 0.93 and newer).

These nodes are then going to be used in the MiniMax algorithm to find the best move.

// Record the node in the Nodes Analysis array (to use with MiniMax algorithm) skakos
NodesAnalysis[NodeLevel_1_count, 1, 0] = Temp_Score_Human_before_2;
NodesAnalysis[NodeLevel_2_count, 2, 0] = Temp_Score_Human_after_2;
NodesAnalysis[NodeLevel_3_count, 3, 0] = Temp_Score_Human_before_4;
NodesAnalysis[NodeLevel_4_count, 4, 0] = Temp_Score_Human_after_4;
NodesAnalysis[NodeLevel_5_count, 5, 0] = Temp_Score_Human_before_6;
NodesAnalysis[NodeLevel_6_count, 6, 0] = Temp_Score_Human_after_6;

For every node, we also store the number of the parent node:

// Store the parents (number of the node of the upper level)
NodesAnalysis[NodeLevel_1_count, 1, 1] = 0;
NodesAnalysis[NodeLevel_2_count, 2, 1] = NodeLevel_1_count;
NodesAnalysis[NodeLevel_3_count, 3, 1] = NodeLevel_2_count;
NodesAnalysis[NodeLevel_4_count, 4, 1] = NodeLevel_3_count;
NodesAnalysis[NodeLevel_5_count, 5, 1] = NodeLevel_4_count;

This is required for the MiniMax algorithm implementation (see http://en.wikipedia.org/wiki/Minimax on how this algorithm works): We start from the lower level nodes and go up to the beginning of the tree, like in the schema that follows:

Suppose the game being played only has a maximum of two possible moves per player each turn. The algorithm generates the tree shown in the figure above, where the circles represent the moves of the computer AI running the algorithm (maximizing player), and squares represent the moves of the human opponent (minimizing player). For the example’s needs, the tree is limited to a look-ahead of 4 moves.

The algorithm evaluates each leaf node using the CountScore evaluation functions, obtaining the values shown. The moves where the maximizing player wins are assigned with positive infinity, while the moves that lead to a win of the minimizing player are assigned with negative infinity (this is again for illustration purposes only – infinity will not happen in the game as it is currently developed). At level 3, the algorithm will choose, for each node, the smallest of the child node values, and assign it to that same node (e.g. the node on the left will choose the minimum between "10" and "+8", therefore assigning the value "10" to itself). The next step, in level 2, consists of choosing for each node the largest of the child node values. Once again, the values are assigned to each parent node. The algorithm continues evaluating the maximum and minimum values of the child nodes alternately until it reaches the root node, where it chooses the move with the largest value (represented in the figure with a blue arrow). This is the move that the player should make in order to minimize the maximum possible loss.

In order for the program to calculate the best move, a number of “for loops” are applied so as to make the abovementioned backwards computation possible.

        for (counter7 = 1; counter7 <= NodeLevel_7_count; counter7++)
        {
            for (counter8 = 1; counter8 <= NodeLevel_8_count; counter8++)
            {
                if (NodesAnalysis[counter8, 8, 1] == counter7)
                {
                    if (counter8 == 1)
                        NodesAnalysis[counter7, 7, 0] = NodesAnalysis[counter8, 8, 0];
 
                    if (counter8 > 1)
                        if (NodesAnalysis[counter8, 8, 0] < NodesAnalysis[counter7, 7, 0])
                            NodesAnalysis[counter7, 7, 0] = NodesAnalysis[counter8, 8, 0];
                }
            }
        }

After the algorithm has reached the root node, the move with the best score is selected.

ComputerMove[Maximum thinking depth] – End

III. Huo Chess Thinking Flow (v0.95 Simple-Minimax)

ComputerMove()
{
 
DangerousSquares
MoveFilter
 
for all possible moves
{
    Temporarily make all possible moves
    Count the score of these moves
    Call ComputerMove2 (with the temporary chessboard as input)
 
    // Second level of thought
    ComputerMove2()
    {
         ...
         ComputerMove5()
         {
         Record the moves and their scores in the Nodes Analysis array
         }
    }
}
 
Do the best move (MiniMax);
}

IV. Huo Chess Thought Flow (v0.93 Kakos-Minimax or all v0.84 and older versions)

In this section, I analyze the thinking algorithm for version 0.93-Kakos-Minimax or for versions 0.84 and older. (the main difference between these are the method used to find the best move after the analysis of all possible moves is complete) Below, I illustrate the step-by-step process of the computer's thought for a thinking depth of 2. Let's see the "step" boxes to understand the way the program is structured.

Scenario Details

  • Computer Level: Maitre (ThinkingDepth = 2)

ComputerMove - Start

Step 1

START

Move_Analyzed = 0

1.       If the first time called -> store initial chessboard position.
2.       if( Move_Analyzed >Thinking_Depth )
3.       Stop_Analyzing = true;
4.       if( Stop_Analyzing = false)
5.       Scan chessboard.
         for iii, jjj
6.       Scan chessboard, find a piece of the HY , conduct move, 
         check correctness and legality of move,
         and if all is OK, then call CheckMove to measure the score of the move.

Call: CheckMove

CheckMove - Start

  1. Number of moves analyzed ++.
  2. Check correctness and legality of move.
  3. Check if there is a mate on the chessboard.
  4. If the move is correct and legal, then do it.
  5. Check if there is a pawn to be promoted.
  6. Store move to ***_HY variables because, after many calls of ComputerMove and CheckMove functions, the initial values of the move analyzed will be lost.
  7. If this is the first move analyzed, then record it as the correct "best" move, no matter how bad it is.

Step 2

IF result: FALSE
Move_Analyzed = 0
  1. if(Move_Analyzed = Thinking_Depth)
  2. Measure the score of the move and record it as "best" move if it is larger than the score of the so-far best move score.

Step 3

IF result: TRUE
Move_Analyzed = 1
  1. if (Move_Analyzed < Thinking_Depth)

HumanMove - Start [HumanMove_Template for v0.93]

Step 4

Find the best answer of the Human (Move_Analyzed = 1).

Version 0.93: Find ALL possible human moves

  • Scan the chessboard -> find any possible move.
  • Call CheckHumanMove. [redundant in v0.93]

Store the chessboard score before and after the human move.

CheckHumanMove - Start

voidCheckHumanMove(array<String^, 2>^ CMSkakiera_Human_Thinking)

Count the score of the move and record it as "best"if its score is better than the so-far best move.
In v0.93 and newer: Record the score before and after the human opponents makes his move.
Those scores are recorded in the Nodes Analysis array and will be used for the MiniMax algorithm at the end (to evaluate the best move).

CheckHumanMove - End

Conduct the best move of the human [conduct all possible human moves in v0.93].

Move_Analyzed = Move_Analyzed + 1;
Who_Is_Analyzed = "HY";
 
for(i = 0; i <= 7;i++)
{
for(j = 0; j <= 7; j++)
{
Skakiera_Move_After[(i),(j)]=Skakiera_Human_Thinking[(i),(j)];
}

Step 5

Move_Analyzed = 2

Step 6

CALL next ComputerMove function for next-level move analysis.

Move_Analyzed = 2
if(Move_Analyzed == 2)
this->ComputerMove2(Skakiera_Move_After);
elseif(Move_Analyzed == 4)
this->ComputerMove4(Skakiera_Move_After);
elseif(Move_Analyzed == 6)
this->ComputerMove6(Skakiera_Move_After);
elseif(Move_Analyzed == 8)
this->ComputerMove8(Skakiera_Move_After);
 
// Call ComputerMove2 to find the best next move of the HY (deeper thinking)

Step 7

Scan the chessboard and find the best move for the computer.

Move_Analyzed = 2
voidComputerMove2(array<STRING^, />^ Skakiera_Thinking_2)
{
// Same as&hellip;ComputerMove
 
if(Move_Analyzed has not reached thinking depth)
{
// Same as&hellip;ComputerMove: Call CheckMove -> HumanMove -> next ComputerMove etc
// (If we haven't reached the desired level of analysis, then the HumanMove
// will be called again, then again the ComputerMove function etc.)
}

Step 8

Return to a previous ComputerMove (i.e. ComputerMove4 calls ComputerMove2) function to continue the analysis.

Move_Analyzed = 2 (at the end of the analysis this variable will be equal to 0, see Step 9).

// Return to the ComputerMove function of the 'previous' thinking
// level to continue the analysis
 
Move_Analyzed = Move_Analyzed - 2;
Who_Is_Analyzed = "HY";
 
for(i = 0; i <= 7; i++)
{
for(j = 0; j <= 7; j++)
{
Skakiera_Thinking[i,j] = Skakiera_Move_0[i,j];
}
}
}

ComputerMove2 - End

HumanMove - End

CheckMove - End

// close for iii, jjj loop
// close if( Stop_Analyzing = false ) segment

If no legal move is found -> we have MATE!

Step 9

Version 0.93: Apply the MiniMax algorithm to reach to the best move.

Play the move with the highest score. Now it is the Human's turn to play.

if (move_analyzed==0){
  1. Check if it is good to conduct castling.
  2. "Redraw" the chessboard with the best move found.
  3. Move the rook next to the king, if castling occurred.
  4. Check if a pawn is promoted (at the current version computer always promotes a pawn to queen).
  5. Now it is the turn of the other player (human) to play!
}
20. else
21. {
22. Move_Analyzed = Move_Analyzed - 2;
23. Who_Is_Analyzed = "HY";
24.
25. for(i = 0; i <= 7; i++)
26. {
27. for(j = 0; j <= 7;j++)
28. {
29. Skakiera_Thinking[i,j] = Skakiera_Move_0[i,j];
30. }
31. }
}

ComputerMove – End

Thinking Flow summary for v0.93 Kakos-Minimax or v0.84 and older versions

ComputerMove()
{
for
{
    // First level of thought
    CheckMove()
    {
    if (Move_Analyzed <Thinking_Depth)
    {
        Move_Analyzed++;
        // Find the best possible human move-answer and continue the thinking tree
        Find Best Human Move (HumanMove function) / Find all possible human moves (v0.93);
         Record chessboard scores before and after the human move;
        Move_Analyzed++;
        Go to next thinking depth
 
            ComputerMove2()
            {
                for
                {
                    CheckMove();
 
                    // Think deeper if you have not reached the thinking depth
                    if (Move_Analyzed <Thinking_Depth)
                        [Think deeper, if necessary!];
 
                    // Record move analyzed as Best Move, if it has the best score
                    if (Move_Analyzed = Thinking_Depth)
                        CountScore();
                        [Record if best move];
                }
 
            // Return to the initial level of thinking to start
            // analyzing a new thread
            Move_Analyzed = Move_Analyzed &ndash; 2;
    }
    }
}
 
Do the best move;
}

V. Huo Chess Micro Edition

I attempted to create a "Micro edition" of Huo Chess by stripping the program off every unnecessary line of code or file. In particular, in order to reduce the size, I used Huo Chess version 0.6 (programmed in C++) (you can find it in the Downloads section in the "Older versions") and did the following:

  1. Reduced all string/text in the program (i.e. "White Rook" => "WR")
  2. Reduced the length of variable names (in every variable in the program, specific strings were replaced with smaller ones). I don't really know why, but this played a role! Try it yourself to see...
  3. Icon was replaced with smaller one
  4. Removed all unnecessary files (resources, assembly.ccp, stdafx, etc.)

With the above steps implemented, the size was reduced from 53.5 KB to 47.5 KB. However, the code of the program can't be read at all! There is a price for small code...

After processing the final executable file with .NETZ (can be obtained for free from http://www.madebits.com/netz/) the size was reduced to a mere 36 KB. .NETZ is a compression tool that started in Codeproject [see http://www.codeproject.com/KB/dotnet/ReduceDotNetSize.aspx]. You can also look at http://www.jot.fm/issues/issue_2005_09/article1/ in order to see how this reduction in size of the PE (portable executable) is realized. The file is distributed as well tagged as 'Micro Edition' (see the downloadable files at the top of the article).

VI. Huo Chess GUI Edition

I created a Huo Chess edition with Graphical User interface (GUI). For the development of that edition, I used the C# and Visual Studio 2012. In previous versions the  XNA Game Studio version 3.0 (based on Microsoft Visual Studio 2008) was used. It consists of a simple chessboard which shows the current chessboard state. It does support mouse move so you do not have to enter the moves via keyboard. More advanced GUI editions with come in the future. It must be noted that the GUI did not affect the total size of the program much. The Huo Chess C# 0.95 GUI edition has a total size of 93 KB in the 0.95 version.

VII. Huo Chess Games Archive

That segment contains games played by Huo Chess versus other micro chess programs.

GAME 1

Date: 2007-11-11
Place: Athens, Greece
White: HuoChess v0.4 (with Opening Book) [as distributed by The Code Project]
Black: Microchess (as provided by BenLo Park)
Result: Draw by threefold repetition

1.       d4 Nc6
2.       d5 Nb4
3.       Nc3 e5
4.       Bg5 Qxg5
5.       Nh3 Qg4
6.       e4 Qh4
7.       Be2 d6
8.       Bb5+ Kd8
9.       Nf4 Qxf4
10.      h3 Nf6
11.      f3 Qe3+
12.      Be2 Bd7
13.      f4 Qg3+
14.      Kd2 Qxf4+
15.      Ke1 Qg3+
16.      Kd2 Qf4+
17.      Ke1 Qg3+
18.      Kd2 Qf4+
19.      Ke1 Qg3+
20.      Kd2 Qf4+
21.      Ke1 [draw by threefold repetition]

GAME 2

Date: 2007-11-11
Place: Athens, Greece
White: Microchess (as provided by BenLo Park)
Black: HuoChess v0.4 (with Opening Book) [as distributed by The Code Project]
Result: Draw by threefold repetition

1.       e4 e6
2.       Qh5 d6
3.       Bb5+ Ke7
4.       Qg5+ f6
5.       Qh5 h6
6.       d4 g6
7.       Qxg6 Nd7
8.       Bf4 f5
9.       exf5 Ndf6
10.      fxe6 Bxe6
11.      a4 Bg7
12.      Qxg7+ Bf7
13.      Qxh8 Bh5
14.      Qg7+ Bf7
15.      Nc3 h5
16.      Nf3 Nh7
17.      Nd5+ Ke6
18.      c4 c6
19.      Nc7+ Qxc7
20.      d5+ cxd5
21.      Nd4+ Ke7
22.      Nf5+ Ke6
23.      Nd4+ Ke7
24.      Nf5+ Ke6
25.      Nd4+ Ke7
26.      Nf5+ Ke6
27.      Nd4+ Ke7
28.      Nf5+ [draw by threefold repetition]

GAME 3

Date: 2008-01-22
Place: Athens, Greece
White: Microchess (as provided by BenLo Park)
Black: HuoChess v0.5 (with Opening Book) [as distributed by The Code Project]
Result: Draw by threefold repetition

1.       e2-e4 e7-e6
2.       d1-h5 c7-c5
3.       f1-b5 g8-f6
4.       h5-e5 f6-g4
5.       e5-f4 g4xf2
6.       e1xf2 g7-g5
7.       f4-e5 a7-a6
8.       b5-c4 f7-f6
9.       e5-g3 d7-d6
10.      g1-h3 h7-h6
11.      h1-e1 e8-e7
12.      b1-a3 d8-b6
13.      d2-d4 b6-a5
14.      c1-d2 a5xd2+
15.      e1-e2 d2xd4+
16.      16.f2-f3 d4xb2
17.      a1-d1 b2xa3+
18.      c2-c3 a3xc3+
19.      c4-d3 a6-a5
20.      f3-g4 e7-d7
21.      d3-b5+ d7-e7
22.      g3xc3 f8-g7
23.      e2-f2 e7-f7
24.      d1xd6 a8-a7
25.      c3xc5 b8-c6
26.      f2-f3 b7-b6
27.      c5xb6 c8-d7
28.      f3-c3 e6-e5+
29.      d6xd7+ a7xd7
30.      b5-c4+ f7-e7
31.      b6-c5+ d7-d6
32.      c3-d3 c6-d4
33.      c5-c7+ d6-d7
34.      c7-c5+ d7-d6
35.      c5-c7+ d6-d7
36.      c7-c5+ d7-d6 [draw by threefold repetition]

GAME 4

Date: 2008-02-22
Place: Athens, Greece
White: Microchess (as provided by BenLo Park)
Black: HuoChess v0.6 (without Opening Book) [as distributed by The Code Project]
Result: Draw by threefold repetition

1.       Nf3 d5
2.       Nc3 Qd6
3.       Na4 Qf4
4.       c3 Bd7
5.       d4 Qe4
6.       Ng5 Qg4
7.       f3 Qh4+
8.       g3 Qh5
9.       Nc5 Bb5
10.      b3 f6
11.      Nge6 b6
12.      e3 Bc6
13.      Nd3 Bd7
14.      Nxf8 Kxf8
15.      Nf4 Qg5
16.      Ne2 Nc6
17.      f4 Qg4
18.      h3 Qf3
19.      Rh2 Nh6
20.      Kd2 Nf5
21.      Kc2 Qe4+
22.      Kb2 Qf3
23.      Kc2 Qe4+
24.      Kb2 Qf3
25.      Kc2 Qe4+

Huo Chess (even without Opening Book) managed to play a very good game, during which it played well-structured chess. It didn't conduct any wrong moves, didn't give up any pieces and had a better position than Microchess at the end of the game (when the threefold repetition resulted in a draw).

GAME 5

Date: 2009-01-25
Place: Athens, Greece
White: Microchess (as provided by BenLo Park)
Black: HuoChess v0.82 (with Opening Book) [as distributed by The Code Project]
Result: Draw by threefold repetition

1. e4   e6
2. Qh5  Qe7
3. Bc4  Kd8
4. d4   a6
5. Bf4  d5
6. exd5 f5
7. dxe6 Bxe6
8. Bxe6 g6
9. Qe2  Nd7
10. Bxc7+ Kxc7
11. Qc4+ Nc5
12. dxc5 Qg7
13. Qf4+ Kc6
14. Qf3+ Kxc5
15. Qd5+ Kb6
16. Qb3+ Kc7
17. Qc4+ Kd6
18. Qd5+ Kc7
19. Qc4+ Kd6
20. Qd5+ Kc7
21. Qc4+ Kd6
22. Qd5+  draw by threefold repetition

Huo Chess played a good game. It did not give up pieces without reason and did not lose chances to kill opponent’s pieces when possible.

History

  • 3 October, 2007 -- Original version posted
  • 15 October, 2007 -- Version 0.2
  • 22 October, 2007 -- Version 0.3
  • 15 November, 2007 -- Version 0.4
  • 25 January, 2008 -- Version 0.5
  • 28 January, 2008 -- License info and download updated
  • 28 February, 2008 -- Article content and downloads updated
  • 3 July, 2008 -- Version 0.721 - Article content and downloads updated
  • 8 January, 2009 -- Version 0.722 - Article content and downloads updated
  • 22 April, 2009 - Version 0.82 - Article content and downloads updated
  • 8 September, 2011 – Version 0.93 (C# only) – MiniMax algorithm update
  • 19 September 2012 – Version 0.95 (C# only) - Update dangerous squares check & CountScore 
  • 12 August 2014 - Version 0.961 (C# only) - Fix problems with MiniMax algorithm

Versioning

Huo Chess is intelligently designed, but it also evolves. Currently, the program is at version 0.95. The timeline of versions and the respective changes conducted in the program's code are depicted below.

Version 0.961 (C#) Changes in Huo Chess 0.961: Fixed problems and bugs related to the MiniMax algorithm.
Version 0.95 (C#)

Changes in Huo Chess 0.95:

1. Merged the CountScore and CountScore_Human in one function. Also added parameters to the function so as to better control its parameterization.

2. Added functions to find the dangerous squares in the chessboard (see FindAttackers and FindDefenders functions). Added a "danger" check in the CountScore function. This will check whether the piece in a square is threatened by one or more pieces of the opponent (see DangerWeight variable)

3. Removed unused variables to eliminate all warnings. Reach a Maintenance Index of 19 (from 16 in the previous versions).

4. Fixed the program to take into account Danger_penalty (and removed the similar danger_penalty which was used in some places and caused confusion, actually nulling the effect of Danger_penalty in CountScore)

5. Uncomment the "Find value of piece in Skakiera(y,u)" part in ComputerMove, so as to find the value of Value_of_piece_in_square.+++

Size: 57 KB (without GUI), 93 KB (with GUI) 

0.93 Versions (only C#)

Implemented the MiniMax algorithm for thinking. Two types of versions are distributed:

  1. Huo Chess (Kakos-MiniMax edition): A version where the existing Kakos algorithm for searching in depth is used, with the MiniMax algorithm added for the evaluation of the best move.
  2. Huo Chess (Simple-MiniMax): A new simpler version where the searching in depth has been written from scratch (in an attempt to "clean" the code). MiniMax algorithm is again used for the evaluation of the best move. In this simpler method, the following changes have taken place: Deleted the CheckMoveCountScore_HumanHumanMoveCheckHumanMove HumanMove_template functions. Reorganized and simply Increased thinking depths from 8 to 20. Reduced the size by using a template ComputerMove function for all thinking depths (instead of ComputerMove2ComputerMove4ComputerMove6, etc.). The C++ edition is still in 0.81 version (separate ComputerMove functions still exist) and XNA / VB versions are still in 0.82 versions and are still distributed for educational purposes. Size of C# edition: 52.5 KB.
Version 0.82 Changed the ComputerMoveHumanMoveCountScoreElegxosOrthotitas functions. Increased thinking depths from 8 to 20. Reduced the size by using a template ComputerMove function for all thinking depths (instead of ComputerMove2ComputerMove4ComputerMove6 etc). The C++ edition is still in 0.81 version (separate ComputerMove functions still exist), for educational purposes. Size of C# edition: 52.5 KB.
Version 0.722 XNA Huo Chess (C# edition) with Graphical User Interface based on XNA.
Version 0.722 cs  Huo Chess port in C# programming language.
Version 0.722 Fixed some issues with the HumanMove function.
Version 0.721 More thinking depth analysis capability has been added to the program. Huo Chess uses the ComputerMove2ComputerMove4ComputerMove6 and ComputerMove8 functions to think at a depth of 8 moves (4 half-moves for the white and 4 half-moves for the black pieces). Size: 56.5 KB
Version 0.6 (Micro edition) It was based on version 0.6. Reduced all string/text (i.e. "White Rook" => "WR"). Reduced the length of variable names (in every variable in the program, specific strings were replaced with smaller ones). Icon was replaced with smaller one. Removed all unnecessary files (resources, assembly.cpp, stdafx, etc.). Size: 47.5 KB
Version 0.6 Removed the empty try...catch statement in line 3959 at HumanMove, which caused the bug at ElegxosNomimotitas not to show. Optimized the CountScore function. Fixed the bug at ElegxosNomimotitas in the part that checks moves of Rook-Queen-Bishop at lines 3143-32173. Added penalty in case the computer moves its pieces next to an opponent' pawn. Made the computer eat the opponent's queen when there is a chance. Lowered the value of the opponent's king, so as to avoid the computer continuously going after him and sacrifice pieces for that. Optimized the CountScore_Human function. Size: 53.5 KB
Version 0.5 Fixed HumanMove function. Optimized CountScore and ComputerMove functions. Size: 51.5 KB
Version 0.4 Added random playing capability. Optimized CheckForWhiteCheck and CheckForBlackCheck functions. Size: 51.0 KB
Version 0.3 Stronger playing capabilities. Added opening book capability. Optimized ElegxosNomimotitas and ElegxosOrthotitas functions. Size: 91.5 KB
Version 0.2 Fixed some bugs due to which computer played illegal moves. Thanks to everyone who gave me feedback! Size: 99.5 KB
Version 0.1 Initial version. Known problems: the program plays some illegal moves sometimes. Not too strong at all. 49.0 KB

 

++Keep coding! 

 

License

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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About the Author

Palavos
Software Developer Kakos Bros Solutions
Greece Greece
Spiros [Spyridon or Spyros are also used] Kakos (huo) lives in Athens, Greece. He is currently working as an IT consultant in a large firm. Begun programming during the Commodore era in MS Basic and is still trying to learn (mostly in C++ and C#)...
He likes chess and has recently bought a new (old) modem for one of his Commodores 128 (yes, he has two of them!) to set up a server based on 8-bit technology. He thinks that when the World Wide Web crashes completely by an alien cyber attack, he will be the only one capable of surfing with his Commodore computer and will eventually save the day...
He likes reading and writting philosophy and is a fond admirer of Aristotle and Alfred Russel Wallace. His main heritage is Harmonia Philosophica.
At his free time he is researching the application of polypyrrole (PPy) in the PCB manufacturing process (through-hole plating) at the National Technical University of Athens - Advanced Materials section.
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GeneralMy vote of 5 Pinmemberfredatcodeproject23-Sep-12 12:21 
GeneralRe: My vote of 5 PinmemberPalavos23-Sep-12 13:22 

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