Programming a chess application in QBasic (QB64)

Interested in chess programming? You are interested in philosophy too! What is thinking? Can AI be conscious? What does it mean to do something? Can the AI understand that it does play chess? Explore the AI related articles in Harmonia Philosophica and understand why you are already a philosopher!

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News

  • 2022-05-06: Huo Chess v0.7 in QBasic with graphics is released. The version addresses Out of Memory issues with the logs of the application.
  • 2021-11-14: Created a GitHub repository of the Huo Chess Quick Basic edition.
  • 2021-03-14: Updated thinking in levels 4 and 5; now extra analysis at these levels only happens if the move entails the capturing of a piece. Updated CountScore to include positional elements in the evaluation. Introduced the notions of mate and stalemate in the thinking process.
  • 2020-12-16: Fixed issue with en passant move.

This is an article on how one can develop a chess program in QBasic.

It is based on Huo Chess that was developed by me and is available as open source in multiple programming languages (C#, Java, older versions in C++ and Visual Basic).

Parts of the article have been posted as separate tutorials (check here). They are all gathered here in a unique article that can provide an overall understanding of the chess programming complexity.

The tutorial is at the point at a very simple/ high level but it will be enriched as time passes by. The initial goal was to have at least a starting point for someone who needs to delve into the chess programming cosmos and this I believe I have managed to achieve.

Why in QBasic?

Well, BASIC is simple and fun!

Do you need more reasons?

Well…

Here is another one:

10 PRINT "Why not?!"
20 GOTO 10

That’s what happens when you mess with BASIC. Seriously now, at the end, the goal here is to present the algorithm. The specific language of the implementation does not play a particular role. When you are at a level to develop the next Stockfish be sure to remember that it was here from where you started your journey…

The tutorial should be read in conjunction with the source code of Huo Chess offered here. The code itself is written in a simple and structured way and it is heavily commented so that even without any prior knowledge one can easily understand how thw algorithm works.

Let’s not waste any more time!

Happy coding!

I. Beginning

Let us begin from the… beginning. And one step at a time, try to see how we can develop our chess program.

Important Notes

  • Refer to QB64 site for questions regarding how QBasic commands work. Essentially they are simple, but in case your are stuck with an error you cannot resolve, this is the place where you will find your answers.
  • Always have the source code open in a separate window. Although the tutorial only goes throug portions of the code to explain how it works, it is always beneficial to have the whole source code of the working program in front of you.
  • A note about the source code versions: Chapter I to IV refer to the version 0.3 of the code. Chapter V refers to the version 0.6 of the code.
  • The Huo Chess Quick Basic edition is available for download either from here or from the relevant Huo Chess QB64 GitHub repository.

And don’t forget: Coding is about experimenting!

Do not wait everything from this tutorial or from any other tutorial! Just open the QBasic editor and write code! Change things! See what they do! At the end it is YOU who is learning, not me teaching you!

Programming is fun.

Want to learn? Why not start now?

And what better way there is than to develop an advanced program that makes the computer “think” how to play chess?! Forget about those silly beginners’ example programs which print “Hello world”. Who in his right mind ever spends time to write a program which prints “Hello world” on the screen?

BASIC is fun.

A very easy language for kids to start learning programming. So we will use it here. (Not in the mood for BASIC? Search for the Java and C# relevant tutorials in Harmonia Philosophica!)

And in one day, you will have learnt the basic of how to program a chess program in BASIC! The actual implementation could take a bit longer but in general in some days you will have the knowledge of how the program works, thus being in a position to improve it or write your own!

The BASIC programming language is one of the simplest ones. Especially designed for simplicity, it uses simple commands to perform operations. The commands are easy to learn and use, since they are utilizing English language words which are easily recognizable. Want to print something on the screen? Use the command PRINT! Want to determine what will happen if something else happens? Use the command IF! See what I mean?

IF YOU_UNDERSTAND = "Yes" THEN PRINT "Yes I understand!"

Did you understand the above command? Great!

The only thing that might trouble you if you are new to programming is the notion of variables. A variable is an element which holds values. This element is used in various places of the program. The values could be of different types – the ones we will use are integers (1, 4, 10…) and strings/ characters (e.g. “white pawn”).

Variables could be stored also in arrays. An array is a collection of variables in one or more dimensions. It is also known as a “table”. And yes, you guessed right: we will use an array (table) to store the chessboard. Could you guess the dimensions of that table? Spot on again.

DIM SHARED chessboard$(8, 8)

The command DIM defines a variable. We do all the variable declarations in the beginning of the program. The above command declares an 8×8 array which holds the… you guessed it right: The chessboard!

By the way, we are using QBasic64, a version of Quick Basic that is available for free and pretty popular as well. Check https://www.qb64.org/portal/ to get it. The site also contains excellent tutorials and training material. You will have it as a reference throughout the whole process, so make sure you create a bookmark out of it.

After you download QBasic64 from the site, just click on the QB64.exe file and execute it. You will be presented with the IDE (programming interface) of QBasic where you will write your program. I do not need to explain the “Create new”, “Save” or “Save as” functions…

Just start writing the commands and then press “Save as”. Executing the program is easy as well. Just select Run > Start.

As said above, what is the first thing to do? (Besides the “Create new program” part and saving it in a folder of your choosing with a name of your choosing as well)

Some of the first things to do we already mentioned.

First, declare the variables. BASIC is really simple in the sense that it is not so strict in requiring you to declare all the variables before using them, in contrast to other high level languages like C#. However this is also a downside of the language. Leaving the programmer with some slack makes the programmer careless.

So we will declare all the variables we use…

DEFINT A-Z
OPTION BASE 1 'Make tables dimensions start from 1
DIM SHARED chessboard$(8, 8)
COMMON SHARED startingRank, startingColumn
COMMON SHARED finishingRank, finishingColumn
COMMON SHARED startingRankText$, startingColumnText$
COMMON SHARED finishingRankText$, finishingColumnText$
COMMON SHARED Move$
COMMON SHARED MovingPiece$
COMMON SHARED debugMode

There you go. (Read the QB64 Wiki for what DEFINT A-Z and OPTION BASE 1 commands do – Remember, learning entails the process of… learning)

What is next?

What else?

Ask the user for input!

And what else to ask than his first move! For simplicity purposes we will present the main command which tells the user to put his move. The drawing of the board on the screen will be done by the drawBoard() function. A function is an independent set of code that you can call to perform an action. In our case we call the drawBoard function (with the command… CALL DRAWBOARD – I told you it was simple). For the time being forget how the function works. (Even though reading through it and trying to understand how it works would be a lesson on its own…)

So how would you ask for the user input?

Simple by the command… INPUT!

There you go:

INPUT "Enter your move: ", Move$

This command tells the computer to wait for the user to enter something and press Enter. When this happens, the text entered by the user is saved in the Move$ variable. The dollar sign ($) indicates that the variable is a string (text) and not a number.

Let’s stay there.

For now you have learnt how to…

  • Create a new program.
  • Declare the variables you will use (including the chessboard).
  • Ask from the user to enter his first move.

You have also been acquainted with the notions of variables and functions and with some basic BASIC (get the joke?) commands, like IF… THEN or INPUT and PRINT. Don’t worry if you don’t get everything yet. You will as you program more and more every day.

Next lessons will include the next logical steps:

  • Validate that the user move is valid and legal.
  • Redraw the chessboard with the move of the user.
  • Make the computer think of an answer.

You would be surprised how the last part (the thinking of the computer) is a rather simple one. In essence the computer thinks of all possible moves, validates them (in the same way we will validate the move entered by the user) and then for the valid ones it will calculate a score of the position. The move with the best score will be the move of the computer.

II. Check legality of a move

In the previous chapter we managed to create our program in BASIC (QB64) and write the first lines of code to ask for input from the user for his move.

Now we will perform the next and one of the most important steps in a chess program: Checking the legality and validity of a move!

Remember that we asked from the user his move by the command…

INPUT "Enter your move: ", Move$

With this command the computer asks for input by the user and then stores what the user entered (after he presses Enter) into the Move$ variable. As we said in the previous lesson, the $ sign in a variable name denotes that the variable stores a string (text) value and not a number.

Now that we have the move into a single text variable, we should first of all “break it down” into four specific elements:

  • Starting column
  • Starting rank
  • Finishing column
  • Finishing rank

In that way we will have the starting and finishing coordinates of the user’s move, which we will use for the checking of the legality of the move later on.

How do we “break up” the text? By using the MID$ built-in function of BASIC which returns a specific character from a text. So if the user entered the move “g2g4” (which is Grob opening by the way, one of my favorites), then the command…

MID$(Move$, 2, 1)

will return the character “2”, since it will start from character 2 of the Move$ variable and will get 1 character.

Without further delay, here is the code which breaks up the move the user entered into the four numbers we are looking for.

INPUT "Enter your move: ", Move$
startingColumnText$ = MID$(Move$, 1, 1)
startingRankText$ = MID$(Move$, 2, 1)
finishingColumnText$ = MID$(Move$, 3, 1)
finishingRankText$ = MID$(Move$, 4, 1)
SELECT CASE startingRankText$
CASE "1"
startingRank = 1
CASE "2"
startingRank = 2
CASE "3"
startingRank = 3
CASE "4"
startingRank = 4
CASE "5"
startingRank = 5
CASE "6"
startingRank = 6
CASE "7"
startingRank = 7
CASE "8"
startingRank = 8
END SELECT
SELECT CASE finishingRankText$
CASE "1"
finishingRank = 1
CASE "2"
finishingRank = 2
CASE "3"
finishingRank = 3
CASE "4"
finishingRank = 4
CASE "5"
finishingRank = 5
CASE "6"
finishingRank = 6
CASE "7"
finishingRank = 7
CASE "8"
finishingRank = 8
END SELECT
SELECT CASE startingColumnText$
CASE "A", "a"
startingColumn = 1
CASE "B", "b"
startingColumn = 2
CASE "C", "c"
startingColumn = 3
CASE "D", "d"
startingColumn = 4
CASE "E", "e"
startingColumn = 5
CASE "F", "f"
startingColumn = 6
CASE "G", "g"
startingColumn = 7
CASE "H", "h"
startingColumn = 8
END SELECT
SELECT CASE finishingColumnText$
CASE "A", "a"
finishingColumn = 1
CASE "B", "b"
finishingColumn = 2
CASE "C", "c"
finishingColumn = 3
CASE "D", "d"
finishingColumn = 4
CASE "E", "e"
finishingColumn = 5
CASE "F", "f"
finishingColumn = 6
CASE "G", "g"
finishingColumn = 7
CASE "H", "h"
finishingColumn = 8
END SELECT

The above code breaks up the Move$ into the above-mentioned four numbers and you would have noticed that the break up has two steps which we didn’t quite mention before: First we break up the text and then we ‘translate’ the four text values into numbers. This is done with the SELECT CASE commands, which essentially select different numeric value for each possible value of the columns and ranks. And yes, the text “8” needs to be translated to the number 8. As far as the computer is concerned they are completely different monsters altogether.

So now we have what we need. The starting column and rank and the finishing ones. How do we check the legality and validity of the move? Well, there is no “magic” way. We just have to write code to do that.

For clarity purposes we will gather all the code performing the check of the legality in one function (= set of code) which we will call ElegxosNomimotitas (= Check of Legality, in Greek). We will define the call parameters of the function (i.e. what parameters we need to pass over the function for it to do its job) and then write the code inside it.

What would be the input parameters?

The starting and end column and ranks of course. And we will also send the Moving Piece in a separate variable because this is a crucial part of the function. Different pieces move in a different way, right?

The function declaration would be something like…

DECLARE SUB ElegxosNomimotitas(ENSkakiera() AS STRING, startColumn AS INTEGER, startRank AS INTEGER, finishColumn AS INTEGER, finishRank AS INTEGER, MovingPieceEN AS STRING)

The ENSkakiera (=Elegxos Nomimotitas Skakiera = Check Legality Chessboard in Greek) is the array of the chessboard (= Skakiera in Greek) we will need to pass. The function will check the legality of a move not in general, but in the context of a specific chessboard of course. Then beyond the chessboard we pass over the start and end columns and ranks and the Moving piece as mentioned above.

Now we are in the function which checks the legality of the move. We will show how to do this for one piece, the rook. Then the logic is similar (not exactly, but you will get the meaning) for the other pieces.

Without further delay, here is the code…

IF (MovingPieceEN$ = "wrook" OR MovingPieceEN$ = "brook") THEN

    IF debugMode = 1 THEN PRINT "Nomimotita = " + STR$(NomimotitaEN)

    'Check correctness of move (Rook only moves in lines)

    IF ((startColumn <> finishColumn) AND (startRank <> finishRank)) THEN NomimotitaEN = 0

    IF debugMode = 1 AND NomimotitaEN = 0 THEN PRINT "Checkpoint ROOK-0"

    'Check if the Rook moves beyond the limits of the chessboard

    IF ((finishColumn < 1) OR (finishRank < 1)) THEN NomimotitaEN = 0
    IF ((finishColumn > 8) OR (finishRank > 8)) THEN NomimotitaEN = 0


    'Check if another piece is between the current and the target square

    'Horizontal movement

    IF (startColumn > finishColumn) AND (startRank = finishRank) THEN
        FOR J = startColumn TO finishColumn STEP -1
            IF (J <> startColumn) AND (J <> finishColumn) AND ENSkakiera(J, startRank) <> "" THEN NomimotitaEN = 0
        NEXT J
    END IF

    IF (startColumn < finishColumn) AND (startRank = finishRank) THEN
        FOR J = startColumn TO finishColumn
            IF (J <> startColumn) AND (J <> finishColumn) AND ENSkakiera(J, startRank) <> "" THEN NomimotitaEN = 0
        NEXT J
    END IF

    'Vertical movement

    IF (startColumn = finishColumn) AND (startRank > finishRank) THEN
        FOR J = startRank TO finishRank STEP -1
            IF (J <> startRank) AND (J <> finishRank) AND ENSkakiera(startColumn, J) <> "" THEN NomimotitaEN = 0
        NEXT J
    END IF

    IF (startColumn = finishColumn) AND (startRank < finishRank) THEN
        FOR J = startRank TO finishRank
            IF (J <> startRank) AND (J <> finishRank) AND ENSkakiera(startColumn, J) <> "" THEN NomimotitaEN = 0
        NEXT J
    END IF

    'If the start square is the same as the destination...
    IF startColumn = finishColumn AND startRank = finishRank THEN NomimotitaEN = 0

    'Check if a piece of the same colour is at the destination square
    IF MID$(ENSkakiera$(finishColumn, finishRank), 1, 1) = MID$(ENSkakiera$(startColumn, startRank), 1, 1) THEN NomimotitaEN = 0

    IF debugMode = 1 AND NomimotitaEN = 0 THEN PRINT "Checkpoint ROOK-5": 'INPUT a$

END IF

This code checks for many things:

  • If the rook moves in rows or columns
  • If the rook moved beyond the limits of the chessboard
  • If the rook is blocked by another piece before he reaches the destination square
  • If there is a piece of the same colour at the destination square.

If any of the above validations fail, then the Nomimotita (legality in Greek) variable is set to false (0). If not, the Nomimotita is 1 (= true). Note that we use an integer variable to indicate the Nomimotita (which takes values 0 or 1) instead of a text variable (which would take the values “True” or “False”) to use less memory. But we could anyway use a text variable with the same result.

Note: The “IF debugMode = 1” lines of code are used to print to the screen messages for debugging.

It may look complicated, but at the end it is not. The code is simple and self explanatory, with comments. Take for example the first validation: A rook must move in columns or ranks. How is this translated in the code?

    IF ((startRank = finishRank) AND (startColumn <> finishColumn)) THEN NomimotitaEN = 0
IF ((startColumn = finishColumn) AND (startRank <> finishRank)) THEN NomimotitaEN = 0
IF ((startColumn <> finishColumn) AND (startRank <> finishRank)) THEN NomimotitaEN = 0

Read the code at your own pace.

At the end it is just… English. 🙂

After making sure that the move entered is valid, then all we have to do is… make it and present it to the screen.

'If move is legal, then do the move and present it in the chessbooard
IF Nomimotita = 1 THEN

    IF debugMode = 1 THEN
        PRINT "Now we will redraw the chessboard"
    END IF

    'Do the move
    chessboard$(finishingColumn, finishingRank) = chessboard$(startingColumn, startingRank)
    chessboard$(startingColumn, startingRank) = ""
    CLS
    CALL drawBoard

END IF

Go on and check the program source code (check at the end of the tutorial to find it), which contains all the other move validity checks for all the other pieces. You will find it pretty easy to read and understand. It contains comments within the code to support you in your lesson. Copy and paste the code in your QBasic interpreter/ compiler to see it and compile it.

III. Chess thinking algorithm

In the previous chapter we managed to create the function that checks the legality of a move. With that we managed to check the move entered by the user and present it to the screen.

What is next?

To make the computer think of an answer!

In essence this is easy! (the difficult part is to make the computer think of a really good move, that we will tackle later on)

What we will need besides the SUB which checks the legality of the move (ElegxosNomimotitas) is a SUB which counts the score of any given position in the chessboard. This will allow us to evaluate all the possible moves, so that the computer can select the best one.

This is done by the code below, which simply scans the chessboard for pieces and adds or subtracts the value of each piece it founds from the total score of the chessboard.

SUB countScore

positionScore = 0

FOR I = 1 TO 8
    FOR J = 1 TO 8

        IF chessboard$(I, J) = "wpawn" THEN positionScore = positionScore + 1
        IF chessboard$(I, J) = "wrook" THEN positionScore = positionScore + 5
        IF chessboard$(I, J) = "wknight" THEN positionScore = positionScore + 3
        IF chessboard$(I, J) = "wbishop" THEN positionScore = positionScore + 3
        IF chessboard$(I, J) = "wqueen" THEN positionScore = positionScore + 9
        IF chessboard$(I, J) = "wking" THEN positionScore = positionScore + 100

        IF chessboard$(I, J) = "bpawn" THEN positionScore = positionScore - 1
        IF chessboard$(I, J) = "brook" THEN positionScore = positionScore - 5
        IF chessboard$(I, J) = "bknight" THEN positionScore = positionScore - 3
        IF chessboard$(I, J) = "bbishop" THEN positionScore = positionScore - 3
        IF chessboard$(I, J) = "bqueen" THEN positionScore = positionScore - 9
        IF chessboard$(I, J) = "bking" THEN positionScore = positionScore - 100

    NEXT J
NEXT I

END SUB

Easy? Pretty much. All that is needed is a nested FOR loop (read in QB64 here how that works). Nothing more.

Now that we have that, let’s move on to the main course.

The ComputerMove SUB, which – you guessed right – makes the computer perform a move!

In essence, the steps needed are pretty simple:

Scan the chessboard.

If you find a piece of the computer, then…

Scan all possible moves of that piece to all squares of the chessboard.

For every possible move, check the legality of that move.

If the move is legal, then make it!

Check the score of the move.

If the score is best than the current best move (in the beginning there obviously no current best move), then this is the current best move!

After you have examined all the possible moves, do the current best move

Simple isn’t it?

How is the scanning of the chessboard and the checking of all possible moves performed? With four nested FOR loops.

Check the code below. It simple scans all the chessboard with the FOR loops of I and J and then, if it finds a piece which belongs to the computer, it scans all possible destination squares with the FOR loops of ii and jj.

How do we determine is the piece we found is one of the computer’s pieces? We compare the first letter of the piece (which would be ‘w’ or ‘b’ for white and black pieces) with the color of the player. If for example the color of the player is ‘w’ (for white) and we encounter a piece ‘brook’, then this is a piece of the computer since it is black – i.e. opposite than the color of the player.

'Scan the chessboard...
FOR I = 1 TO 8
    FOR J = 1 TO 8

        'If you find a piece of the computer...
        IF ((MID$(chessboard$(I, J), 1, 1) = "w" AND playerColor$ = "b") OR (MID$(chessboard$(I, J), 1, 1) = "b" AND playerColor$ = "w")) THEN

            'Scan all possible destination squares...
            FOR ii = 1 TO 8
                FOR jj = 1 TO 8

                    startingColumn = I
                    startingRank = J
                    finishingColumn = ii
                    finishingRank = jj

                    MovingPiece$ = chessboard$(I, J)
                    ProsorinoKommati$ = chessboard$(ii, jj)

                    'Check legality of the move entered
                    CALL ElegxosNomimotitas(chessboard$(), 0, startingColumn, startingRank, finishingColumn, finishingRank, MovingPiece$)

                    'If move is legal, then do the move and present it in the chessbooard
                    IF Nomimotita = 1 THEN

                        'Do the move
                        chessboard$(finishingColumn, finishingRank) = chessboard$(startingColumn, startingRank)
                        chessboard$(startingColumn, startingRank) = ""

                        'Count the score of the move
                        CALL countScore

                        'If the score is better than the existing best score, then this is the best move now (and the best score)
                        IF ((playerColor$ = "b" AND positionScore >= bestPositionScore) OR (playerColor$ = "w" AND positionScore <= bestPositionScore)) THEN
                            bestStartingRank = startingRank
                            bestStartingColumn = startingColumn
                            bestFinishingRank = finishingRank
                            bestFinishingColumn = finishingColumn
                            bestPositionScore = positionScore
                        END IF

                    END IF

                    'Undo the move
                    chessboard$(startingColumn, startingRank) = MovingPiece$
                    chessboard$(finishingColumn, finishingRank) = ProsorinoKommati$

                NEXT jj
            NEXT ii

        END IF

    NEXT J
NEXT I

If the move analyzed is legal (the ElegxosNomimotitas SUB is called to determine that) then the move is performed. The score of the position resulting after that is counted (the CountScore SUB is called for that). If the score is better than the current ‘best score’ (the initial best score is zero of course) then this move is registered as best move.

After the scanning is complete, we simply perform the best move!

'Do the best move found
chessboard$(bestFinishingColumn, bestFinishingRank) = chessboard$(bestStartingColumn, bestStartingRank)
chessboard$(bestStartingColumn, bestStartingRank) = ""
CLS
CALL drawBoard

Easy? Yes!

IV. Advanced concepts (Part 1)

The last chapters (Advanced concepts Part 1 and 2) will touch-base on some more advanced concepts in chess programming:

  • Thinking for more moves ahead in the game
  • Improving the position evaluation
  • Selecting the best move (miniMax concept introduction)

Up to now the program we have developed (see the previous lessons, where you can download the program) can think in one (1) move depth. This means that it simply (well, not ‘simply’ – we have gone a long way to make this happen) scans all the possible moves and then selects the one with the highest score.

This has just gave us an insight of the way a computer may think for chess, but a very limited one. The basic thing for thinking for chess is thinking in depth. Everybody would agree that thinking in more depth makes someone a better player.

How can we make that happen?

Simple!

We will copy-and-paste (sort of speak) the main computerMove SUB (Move depth 1 – Existing) two times more, so that the computer also thinks two moves more in depth. In essence, there will be two additional ‘thinking SUB-routines’: One which thinks of the potential human moves (Move depth 2) and another that thinks of the reactions of the computer to those moves (Move depth 3).

After that we will have the following structure:

DEPTH 1: computerMove: This routine thinks of the potential moves of the computer at the first level of thinking. If the thinkingDepth is not reached (i.e. if it is not set to 1, but e.g. 3) then the HumanMove1 SUB is called.

IF Move = thinkingDepth THEN
   'If the score is better than the existing best score, then this is the best move now (and the best score)
   IF ((playerColor$ = "b" AND positionScore >= bestPositionScore) OR (playerColor$ = "w" AND positionScore <= bestPositionScore)) THEN
           bestStartingRank = startingRank
           bestStartingColumn = startingColumn
           bestFinishingRank = finishingRank
           bestFinishingColumn = finishingColumn
           bestPositionScore = positionScore
   END IF
END IF

IF Move < thinkingDepth THEN CALL HumanMove1(chessboard$())

DEPTH 2: HumanMove1: This routine is scanning for all the possible answers of the human opponent. For each of those movements, the next thinking depth routine is called.

DEPTH 3: ComputerMove2: The last routine. Searches for possible moves at depth 3 (i.e. 3 half-moves). The move which ends up with the position with the best score at the end, is the one chosen.

IF ((playerColor$ = "b" AND positionScore >= bestPositionScore) OR (playerColor$ = "w" AND positionScore <= bestPositionScore)) THEN
          bestStartingRank = startingRank
          bestStartingColumn = startingColumn
          bestFinishingRank = finishingRank
          bestFinishingColumn = finishingColumn
          bestPositionScore = positionScore
END IF

With these you will have the program think in depth of 3 half-moves (i.e. a move of the computer, a move of the human opponent and a last move by the computer).

We have been slowly progressing.

Yet, there is a lot more way to go…

V. Advanced concepts (Part 2): Improving the computer thinking with MiniMax

The algorithm described above is simple and does make the computer think in depth. When using the code below, you will see an obvious latency in computer’s thinking vis-a-vis the time needed by the previous version which thinks in depth of only 1 move.

When using the code however one can see that it does not make good moves! Simply by checking the score of the positions at the end, does not lead to wise decisions. The computer thinks, but not very well.

Yet.

What is needed for that to happen?

First improvement: Improvement of the position evaluation.

Now we are just counting the material in the final position.

SUB countScore

positionScore = 0

FOR I = 1 TO 8
    FOR J = 1 TO 8

IF chessboard$(I, J) = "wpawn" THEN positionScore = positionScore + 1
IF chessboard$(I, J) = "wrook" THEN positionScore = positionScore + 5
IF chessboard$(I, J) = "wknight" THEN positionScore = positionScore + 3
IF chessboard$(I, J) = "wbishop" THEN positionScore = positionScore + 3
IF chessboard$(I, J) = "wqueen" THEN positionScore = positionScore + 9
IF chessboard$(I, J) = "wking" THEN positionScore = positionScore + 100

IF chessboard$(I, J) = "bpawn" THEN positionScore = positionScore - 1
IF chessboard$(I, J) = "brook" THEN positionScore = positionScore - 5
IF chessboard$(I, J) = "bknight" THEN positionScore = positionScore - 3
IF chessboard$(I, J) = "bbishop" THEN positionScore = positionScore - 3
IF chessboard$(I, J) = "bqueen" THEN positionScore = positionScore - 9
IF chessboard$(I, J) = "bking" THEN positionScore = positionScore - 100

    NEXT J
NEXT I

END SUB

But material is not everything.

For example in the initial position all moves seem to result in the same material score, however it is known that in the chess opening the good player always moves hies pieces near the center of the chess board, avoid unnecessary movements of the king and the queen and avoids to move the same piece twice.

EXERCISE: Try to improve the countScore SUB to cater for the above. You might need to also introduce a new variable which will count the number of moves we are in the game, so that the computer knows if we are at the opening, the middle of the final stage of the game.

Second improvement: Apply the MiniMax algorithm

Improving the CountScore SUB does not solve our problems.

The thinking mechanism of the program we have desribed is inefficient. One could say that it is inherently flawed.

The program searches for the best score at the end of 3 half-moves, but does not cater at all for the fact that the human opponent moves (at move depth 2) will not be the ones which maximize the score for the computer, but the ones maximizing the score for the human opponent.

In other words: The variants (set of moves) which include completely stupid human opponent moves end up in high scores in favor of the computer and that is why, those variants will at the end be chosen.

But in real life the human opponent will never play his worst moves, but quite the opposite: He will play his best moves so as to win the computer!

The following example will better illustrate the problem.

Imagine we are at the following position…

Important note: The Huo Chess program was developed with simple text-like ‘graphics’. There is also a graphics-version based on Deep Basic chess program (by Thomas McBurney), which you can also download.

An initial version of Huo Chess which simply thought in terms of the best score at the end of 3-half-moves, would play… Qf7+ in this position.

But this is a bad move! Why does it play that?

Simple.

The computer assumes that at the next move human will play something stupid and then it will be able to capture the king of the opponent. The set of moves “1. Qf7+ [something stupid] 2. Qxe8” results in the best (highest) score for the computer, so this move is selected.

The algorithm did not even think that human after Qf7+ will simply play KxQf7 and take the queen, simply because this move results in low score for the computer.

But how do you take into account the best moves for the human opponent (MAXimize value) and at the same time the worst possible variants for the computer (MINImize value)? In other words, at the end, how can the computer play its best move taking into account the worst scenario, which we will have only if the human opponent plays his best move?

EXERCISE: Try to think of the above on your own, WITHOUT reading online about the MiniMax algorithm. First of all – who knows? – you might think of something better! Secondly, one only masters knowledge when he has tried on his own and failed to acquire it. Having things ready for you simply does not lead to wisdom.

The solution to the above problem is the MinMax algorithm. The current version of Huo Chess utilizes it, so you can read there how it works. However note that you can never learn anything by simply copying the program of someone else. Unless you write the program on your own, make mistakes, spend countless nights and days pondering upon them, you will never truly develop anything.

In the MinMax algorithm, we start from the lower level nodes and go up to the beginning of the tree, like in the schema that follows:

MinMax algorithm [Source: Wikipedia]

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 half-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. (source)

In simpler words: It is not enough to calculate the best end-score of a node! Because this might be the result of a combination of good computer moves and bad human moves. The human will not make stupid moves (or at least this will have to be our assumption). So in the level that the computer plays, we must maximize the score (the computer makes the best possible move), but in the levels the human opponent plays we must minimize the score of the computer (i.e. the human plays the best move for him, so the worst move for the computer).

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.

Make sure you check the relevant Huo Chess C# tutorials or the page in Codeproject which refer in more depth to the algorithm (which stays the same regardless of the programming language you use of course).

More updates coming soon!

Download Source Code

This section contains the source code of the Huo Chess QBasic application. Make sure to check here frequently for the latest updates.

Huo Chess source

The Huo Chess source code can be found below. Copy and paste the code in your QBasic interpreter/ compiler to see it and compile it.

One can also download a version with more elaborate graphics for the chessboard, where I have re-used the graphics from the Deep Chess program (by Thomas McBurney). Note that all the code is the same as the Huo Chess above, only the graphics are added – so if you need to study the engine the Huo Chess edition without these more elaborate graphics will perfectly fit your needs.

Important Note: For it to work, copy the PIECES.EGA file you will find in the Deep Chess site to the same folder as your program (this file contains the images of the pieces).

For historical purposes I have added previous versions of the code. You do not have to worry about those. Simply choose the latest one. Make sure you keep on coming to this page for updates of the code.

Important Notes

  • The “HUO Chess with graphics v0.6” needs a lot of memory for the logs so in some systems it may throw an Out of Memory exception. In those cases, please use the “Huo Chess v0.7” which has reduced memory needs instead.

Opening Book Editor

The latest versions (v0.6) of the code have an opening book functionality. In order to generate the files of the opening book you can use the Opening Book Editor found below.

Simply run the program, play the moves you want to store in the opening book and they will all be stored in separate txt files. These files must then be places in the same folder as the Huo Chess executable for the latter to utilize them.

As with Huo Chess, make sure you visit this page again to check for any updates or improvements in the code!

Other Huo Chess resources

Other Huo Chess related resources are listed in this section.

Feel free to write for any comments or suggestions for the program.

Until we meet again…

Happy coding!

HUO Writer: A simple AI writer with basic Neural Network capabilities (QB64)

News

  • 2021-02-14: Updated phrases the AI uses. Added more comments to make the code more readable and easier to configure new phrases.

Goal

The goal of Huo Writer is to be a program that thinks philosophically. By getting user input on a specific topic, it can generate phrases with deep philosphical meaning so as to trigger thoughts on the human user.

The goal of the article is to serve as a simple tutorial on how to build a program that can have a conversation with a human, for people with limited or no programming skills.

An Appendix describes how the program can be enhanced with neural network capabilities, by showing a totally primitive way of helping the program improve its responses based on human feedback.

Go directly at the end to get the code of the program.

Other programming tutorials

Introduction

Programming is easy. And difficult.

It is easy in the sense that all you have to do in order to do it, is… do it! As in all things. There is no magic words to use, no secret key, no hidden doors to unlock. Simply open your computer and start programming. As simple as getting up in the morning and making coffee.

Of course, you must know how to make coffee.

That is the difficult part of programming. But do not worry. As in all other programming tutorials in our portal, we will try to keep it simple.

First of all, we will use the simplest high-level language for our tutorial, i.e. QBasic (and in particular, the 64-bit version which can run in any modern Operating System, QB64). BASIC is, as the name implies, basic. Very easy to learn. There are tons of resources in the Internet on the language; be sure to search for QB64 (QBasic 64 bit) mainly, so as to make sure you search for the right thing.

Secondly, the program itself is ultra-simple. The code is based on a very limited set of commands (mainly PRINT, IF, CASE, INPUT, RANDOMIZE) which you can easily learn. And even if you don’t you are able to understand what they do by reading this article!

Huo Writer on faith, problems, helping…

Yet and despite the above, the actual result is very good! The program actually does sound like a philosopher and writes deep stochastic phrases which will trouble you and invoke thoughts.

Look at the text below…

Never do faith supports us.
Would faith exist without you?
Ignorant men.
Others create reality.
Don’t you remember?
Stop thinking. And you will see…

~ HUO Writer, 2020-09-01

It was written by the program when discussing about the tags faith, helping, problems. Taking into account how simple and ultra-light the program is, it is really good! Don’t you agree? (Note the small gramatical errors, these are still part of the problems still to be solved)

Is it Artificial Intelligence though?

Well, surely it wasn’t created on its own, so yes it is artificial. And it does include some very basic rules to randomly generate what it says. Last but not least, the result seems like it is intelligent, so that is the greatest argument in favor of answering yes to this question. After all, how to I know that you are not robot?

I. How the program works

The program works in the following simple steps:

  1. Ask input from the user: The program needs from the user to enter some tags related to topic of discussion. For each tag, the gender (male, female, neutral, adjective, indifferent) and the number (plural, single, indifferent) must be defined. Note that some of the values are not exactly ‘correct’, for example the ‘adjective’ is not a gender. The values entered are then used by the program to properly formulate the phrases it will generate to speak with the human.
  2. Generate the phrases to say, by not-fully randomly combining the tags provided by the user with specific words and phrases in the database of the program.

Simple huh?

Well, it is!

The most difficult part is to enter meaningful and cleverly selected words and phrases in the database so that the phrases generated by the program can sound as philosophical and meaningful. As in any other thing, the ‘Garbage in-Garbage out’ principle stands strong.

II. Step 1: Get user input

Getting user input is as simple as using the… INPUT command in QBasic and ask from the user the input.

Just see the code below on how the program asks from that input.

PRINT "What is our theme of discussion to-day?"
PRINT ""
INPUT "Number of tags: ", tagsNo
PRINT "Thank you"
SLEEP 2
PRINT "": PRINT "Now…": PRINT ""
SLEEP 2
PRINT "Please enter related tags one by one…"
SLEEP 2
PRINT "One word only per tag please. All in small letters."
PRINT "Enter Bye or just press Enter to exit."
'Read related tags for the subject of discussion
FOR I = 1 TO tagsNo

PRINT ""
INPUT "Enter Tag: ", Tags$(I, 1)
IF Tags$(I, 1) = "Bye" OR Tags$(I, 1) = "" THEN END

TagGender:
INPUT "ENTER THE TAG'S GENDER (m, f, n, a, i) : ", Tags$(I, 2)
IF Tags$(I, 2) <> "m" AND Tags$(I, 2) <> "f" AND Tags$(I, 2) <> "n" AND Tags$(I, 2) <> "a" AND Tags$(I, 2) <> "i" THEN GOTO TagGender

TagNumber:
INPUT "ENTER THE NUMBER OF THE TAG (s, p, i) : ", Tags$(I, 3)
IF Tags$(I, 3) <> "s" AND Tags$(I, 3) <> "p" AND Tags$(I, 3) <> "i" THEN GOTO TagNumber

NEXT I

Initially the program asks for the number of the tags to be entered and then, for each tag (hence the FOR… NEXT loop), the user is asked to enter the parameters (gender, number) of the tag.

As you can see, all the tags entered as placed inside the Tags$ table. This table will be then used to generate the phrases.

The tag itself is stored in dimension 1 of the table, while the parameters of the tag are stored in the dimensions 2 and 3 of the table.

Tags table dimentions

  • Dimension 1: The tag itself
  • Dimension 2: This stores the gender
  • Dimension 3: This stores the number

These parameters will be used later on in validation rules necessary for the correct creation of the phrases.

III. Step 2: Generating the phrases

Based on the input of the user, the program combines the tags with specific phrases or words in the database of the program.

The program generates the following phrases:

  1. Phrase H.1: The first phrase, combining phrases from tables H1$, H2$, one of the tags and a phrase from table H3$.
  2. Phrase H.2: The second phrase, combining a phrase from table H4$, one of the tags and a phrase from table H5$.
  3. Intermediate phrase 1: A connecting phrase, which includes a phrase from table I1$.
  4. Phrase K: The third phrase, combining phrases from tables K1$, K2$ and K3$.
  5. Intermediate phrase 2: A connecting phrase, which includes a phrase from table I2$.
  6. Terminating phrase: The closing phrase, which includes a phrase from table T$.

As mentioned, the elements used for each phrase as documented in the program’s database. By ‘database’ we do not refer to a relational database, but to tables (e.g. tables H1$, H2$, H3$ used for the first phrase) which are populated with data with the code of the program.

These rables are filled in as follows…

'PHRASE 3 (Phrase K)
'Set Variables for third phrase (Phrase K) generation
'----------------------------------------------------
K1$(1, 1) = "Death": K1$(1, 2) = "i": K1$(1, 3) = "i"
K1$(2, 1) = "Life": K1$(2, 2) = "i": K1$(2, 3) = "i"
K1$(3, 1) = "Self": K1$(3, 2) = "i": K1$(3, 3) = "i"
K1$(4, 1) = "God": K1$(4, 2) = "i": K1$(4, 3) = "i"
K1$(5, 1) = "Existence": K1$(5, 2) = "i": K1$(5, 3) = "i"
K1$(6, 1) = "Being": K1$(6, 2) = "i": K1$(6, 3) = "i"
K1$(7, 1) = "Reality": K1$(7, 2) = "i": K1$(7, 3) = "i"
K1$(8, 1) = "Others": K1$(8, 2) = "i": K1$(8, 3) = "p"
K1$(9, 1) = "Thought": K1$(9, 2) = "i": K1$(9, 3) = "i"
K1$(10, 1) = "Knowledge": K1$(10, 2) = "i": K1$(10, 3) = "i"
'-----------------------------------------------------
K2$(1, 1) = " defines ": K2$(1, 2) = "i": K2$(1, 3) = "s"
K2$(2, 1) = " is defined by ": K2$(2, 2) = "i": K2$(2, 3) = "s"
K2$(3, 1) = " creates ": K2$(3, 2) = "i": K2$(3, 3) = "s"
K2$(4, 1) = " is created by ": K2$(4, 2) = "i": K2$(4, 3) = "s"
K2$(5, 1) = " define ": K2$(5, 2) = "i": K2$(5, 3) = "p"
K2$(6, 1) = " are defined by ": K2$(6, 2) = "i": K2$(6, 3) = "p"
K2$(7, 1) = " create ": K2$(7, 2) = "i": K2$(7, 3) = "p"
K2$(8, 1) = " are created by ": K2$(8, 2) = "i": K2$(8, 3) = "p"
'------------------------------------------------
K3$(1, 1) = "death.": K3$(1, 2) = "i": K3$(1, 3) = "i"
K3$(2, 1) = "life.": K3$(2, 2) = "i": K3$(2, 3) = "i"
K3$(3, 1) = "self.": K3$(3, 2) = "i": K3$(3, 3) = "i"
K3$(4, 1) = "being.": K3$(4, 2) = "i": K3$(4, 3) = "i"
K3$(5, 1) = "others.": K3$(5, 2) = "i": K3$(5, 3) = "i"
K3$(6, 1) = "existence.": K3$(6, 2) = "i": K3$(6, 3) = "i"
K3$(7, 1) = "reality.": K3$(7, 2) = "i": K3$(7, 3) = "i"
K3$(8, 1) = "God.": K3$(8, 2) = "i": K3$(8, 3) = "i"
K3$(9, 1) = "thought.": K3$(9, 2) = "i": K3$(9, 3) = "i"
K3$(10, 1) = "knowledge.": K3$(10, 2) = "i": K3$(10, 3) = "i"

As you can see, as with tags, dimensions 2 and 3 are used to store the gender and the number of the elements.

The tables documented above are for the creation of the K Phrase (i.e. the third phrase). The program randomly selects an element from each table and finally creates the phrase to show to the use.

The phrases are generated, by generating random number and then using these numbers to get the relevant entries from the tables defined above.

An example of the code used to get random numbers and create the phrases (the example is from Phrase K) is depicted below.

'----------------------
'Generate third phrase
'----------------------
PhraseK:
RANDOMIZE TIMER
A = INT(RND * 9) + 1: B = INT(RND * 7) + 1: C = INT(RND * 9) + 1
IF C = A THEN GOTO PhraseK
'If number is not equal (plural with plural, single with single) or the number of the next element is not indifferent, then generate phrase again
IF K1$(A, 3) <> K2$(B, 3) AND K2$(B, 3) <> "i" THEN GOTO PhraseK

As said before, the code is simple. In most cases it is. It is the idea that could be complex either in percieving it, implementing it or both. (or selling it I would say, but that is part of another bigger discussion)

Phrases generation rules

There are some basic validation rules applied for the phrases generation. As the program progresses, these rules will be amended and improved.

One rule for example is the following: the number of an element must match with the number of the tag combined with that element. The code below does exactly that thing: If the number (i.e. the 3rd dimension) of the first and the second element do not match, then the program is instructed to go back and generate the phrase again.

IF K1$(A, 3) <> K2$(B, 3) AND K2$(B, 3) <> "i" THEN GOTO PhraseK

If the validation fails, then the program generates a new phrase.

Printing the phrase

After the phrases are generated, they are printied on the screen. With what else than the… PRINT command.

Voila!

A philospher at your own hands!

APPENDIX I – Neural Network

Neural networks are all about having a program learn from its mistakes. Usually this happens with feedback from its user, while the most advanced programs have the ability to learn also on their own. I attempted to start implementing a primitive type of neural network capability.

If the user activates the Learning Mode, the program asks for input after each phrase it produces. If the input is negative (i.e. the user says that he did not like the phrase generated) then the program stores the combination in a table storing ‘bad’ combinations with the code below.

PRINT "": PRINT "Was this sentence a good one? (y/n)"
e$ = ""
DO
    DO
        hyn$ = INKEY$
    LOOP WHILE hyn$ = ""
    SELECT CASE hyn$
        CASE "y", "Y"
            e$ = ""
        CASE "n", "N"
            BadCombinationsK(Kcounter, 1) = A
            BadCombinationsK(Kcounter, 2) = B
            BadCombinationsK(Kcounter, 3) = C
        CASE ELSE
            e$ = "A"
    END SELECT
LOOP WHILE e$ <> ""
PRINT "Thank you for your input.": PRINT ""

The next time the program speaks with the user, the bad combinations are not used in the phrase generation. In that sense, the program learns from the human input. One can visualize the nodes and their combinations are human brain neurons – the neurons which are not used often are gradually disabled while the neurons which are used often are strengthened. Now the program only has the capability to totally ‘delete’ one set of nodes from the possible responses, but future enhancements could see it behave in a more elaborate way.

An example of the code doing that is depicted below.

'Neural network: Check if the combination selected was discarded by human previously. If yes, generate a different one!
FOR I = 1 TO Kcounter
IF BadCombinationsK(I, 1) = A AND BadCombinationsK(I, 2) = B AND BadCombinationsK(I, 3) = C THEN GOTO PhraseK
NEXT I

As mentioned above, this is the simplest form of a neural network: The program ‘learns’ from human interaction and adjusts the ‘nodes’ (elements used for the phrases) inside its ‘brain’ (tables holding the elements used for the phrases) accordingly.

Note that this works only inside the same instance of the program. Whenever you restart the program the ‘bad nodes’ are forgotten, since they are not stored permanently anywhere.

Improvements that are coming soon

  • Make the learning not only disable nodes (0 or 1), but also decrease the likelihood of a node being used (with probability values between 0 and 1).
  • Store what the program has learned into a txt file, so that the next time you execute it, it still ‘remembers’ what it has learnt.

APPENDIX II – The source code

You can click at the link below to get the source code.

Simply copy-paste it into a QBasic editor and execute.

APPENDIX III – How to configure the program

The program is easy to configure. And that is why it is so fun! I have added phrases which are related to my personality and the way I am thinking. You can alter them to reflect yours!

You can also add new elements for the phrases! Simple go and add new elements in the relative tables. When doing that, rememeber to also define the gender and number of the element.

After having added the new element, do not forget to increase the relative constant which defines the size of the table! The constants defining the size of the elements’ tables can be found in the beginning of the program.

Programming for kids: Developing a chess program in BASIC – Part 4: Advanced concepts

Interested in chess programming? You are interested in philosophy too! What is thinking? Can AI be conscious? What does it mean to do something? Can the AI understand that it does play chess? Explore the AI related articles in Harmonia Philosophica and understand why you are already a philosopher!

Hello again!

After having published various tutorials on basic chess programming, it is time to move on to more advanced concepts.

Programming for kids – How to program a chess program in BASIC

Existing chess tutorials

Huo Chess resources

This article will touch-base on some more advanced concepts in chess programming:

  • Thinking for more moves ahead in the game
  • Improving the position evaluation
  • Selecting the best move (miniMax concept introduction)

Up to now the program we have developed (see the previous lessons, where you can download the program) can think in one (1) move depth. This means that it simply (well, not ‘simply’ – we have gone a long way to make this happen) scans all the possible moves and then selects the one with the highest score.

This has just gave us an insight of the way a computer may think for chess, but a very limited one. The basic thing for thinking for chess is thinking in depth. Everybody would agree that thinking in more depth makes someone a better player.

How can we make that happen?

Simple!

We will copy-and-paste (sort of speak) the main computerMove SUB (Move depth 1 – Existing) two times more, so that the computer also thinks two moves more in depth. In essence, there will be two additional ‘thinking SUB-routines’: One which thinks of the potential human moves (Move depth 2) and another that thinks of the reactions of the computer to those moves (Move depth 3).

After that we will have the following structure:

DEPTH 1: computerMove: This routine thinks of the potential moves of the computer at the first level of thinking. If the thinkingDepth is not reached (i.e. if it is not set to 1, but e.g. 3) then the HumanMove1 SUB is called.

IF Move = thinkingDepth THEN
   'If the score is better than the existing best score, then this is the best move now (and the best score)
   IF ((playerColor$ = "b" AND positionScore >= bestPositionScore) OR (playerColor$ = "w" AND positionScore <= bestPositionScore)) THEN
           bestStartingRank = startingRank
           bestStartingColumn = startingColumn
           bestFinishingRank = finishingRank
           bestFinishingColumn = finishingColumn
           bestPositionScore = positionScore
   END IF
END IF

IF Move < thinkingDepth THEN CALL HumanMove1(chessboard$())

DEPTH 2: HumanMove1: This routine is scanning for all the possible answers of the human opponent. For each of those movements, the next thinking depth routine is called.

DEPTH 3: ComputerMove2: The last routine. Searches for possible moves at depth 3 (i.e. 3 half-moves). The move which ends up with the position with the best score at the end, is the one chosen.

IF ((playerColor$ = "b" AND positionScore >= bestPositionScore) OR (playerColor$ = "w" AND positionScore <= bestPositionScore)) THEN
          bestStartingRank = startingRank
          bestStartingColumn = startingColumn
          bestFinishingRank = finishingRank
          bestFinishingColumn = finishingColumn
          bestPositionScore = positionScore
END IF

With these you will have the program think in depth of 3 half-moves (i.e. a move of the computer, a move of the human opponent and a last move by the computer).

You can download the program below.

Download Source Code

Simply copy and paste the code in your QBasic editor and try it out!

One can also download a graphics version, where I have added the graphics engine by Deep Chess (by Thomas McBurney).

Important: For it to work, copy the PIECES.EGA file you will find in the Deep Chess site to the same folder as your program (this file contains the images of the pieces).

Make sure you keep on coming to this page for updates of the code.

Opening Book Editor

The latest versions (v0.6) of the code have an opening book functionality. In order to generate the files of the opening book you can use the Opening Book Editor found below.

Simply run the program, play the moves you want to store in the opening book and they will all be stored in separate txt files. These files must then be places in the same folder as the Huo Chess executable for the latter to utilize them.

As with Huo Chess, make sure you visit this page again to check for any updates or improvements in the code!

Advanced concepts: Improving the computer thinking

The algorithm described above is simple and does make the computer think in depth. When using the code below, you will see an obvious latency in computer’s thinking vis-a-vis the time needed by the previous version which thinks in depth of only 1 move.

However it does not make good moves!

What is needed for that to happen?

First improvement: Improvement of the position evaluation.

Now we are just counting the material in the final position.

SUB countScore

positionScore = 0

FOR I = 1 TO 8
    FOR J = 1 TO 8

IF chessboard$(I, J) = "wpawn" THEN positionScore = positionScore + 1
IF chessboard$(I, J) = "wrook" THEN positionScore = positionScore + 5
IF chessboard$(I, J) = "wknight" THEN positionScore = positionScore + 3
IF chessboard$(I, J) = "wbishop" THEN positionScore = positionScore + 3
IF chessboard$(I, J) = "wqueen" THEN positionScore = positionScore + 9
IF chessboard$(I, J) = "wking" THEN positionScore = positionScore + 100

IF chessboard$(I, J) = "bpawn" THEN positionScore = positionScore - 1
IF chessboard$(I, J) = "brook" THEN positionScore = positionScore - 5
IF chessboard$(I, J) = "bknight" THEN positionScore = positionScore - 3
IF chessboard$(I, J) = "bbishop" THEN positionScore = positionScore - 3
IF chessboard$(I, J) = "bqueen" THEN positionScore = positionScore - 9
IF chessboard$(I, J) = "bking" THEN positionScore = positionScore - 100

    NEXT J
NEXT I

END SUB

But material is not everything.

For example in the initial position all moves seem to result in the same material score, however it is known that in the chess opening the good player always moves hies pieces near the center of the chess board, avoid unnecessary movements of the king and the queen and avoids to move the same piece twice.

Make sure to check the latest version of Huo Chess uploaded, to see how qualitative criteria were added in the countScore SUB!

EXERCISE: Try to improve the countScore SUB to cater for the above. You might need to also introduce a new variable which will count the number of moves we are in the game, so that the computer knows if we are at the opening, the middle of the final stage of the game.

Second improvement: Apply the MiniMax algorithm

The thinking mechanism of the program we have desribed is inefficient. One could say that it is inherently flawed.

The program searches for the best score at the end of 3 half-moves, but does not cater at all for the fact that the human opponent moves (at move depth 2) will not be the ones which maximize the score for the computer, but the ones maximizing the score for the human opponent.

In other words: The variants (set of moves) which include completely stupid human opponent moves end up in high scores in favor of the computer and that is why, those variants will at the end be chosen.

But in real life the human opponent will never play his worst moves, but quite the opposite: He will play his best moves so as to win the computer!

The following example will better illustrate the problem.

Imagine we are at the following position…

Important note: The Huo Chess program was developed with simple text-like ‘graphics’. There is also a graphics-version based on Deep Basic chess program (by Thomas McBurney), which you can also download.

An initial version of Huo Chess which simply thought in terms of the best score at the end of 3-half-moves, would play… Qf7+ in this position.

But this is a bad move! Why does it play that?

Simple.

The computer assumes that at the next move human will play something stupid and then it will be able to capture the king of the opponent. The set of moves “1. Qf7+ [something stupid] 2. Qxe8” results in the best (highest) score for the computer, so this move is selected.

The algorithm did not even think that human after Qf7+ will simply play KxQf7 and take the queen, simply because this move results in low score for the computer.

But how do you take into account the best moves for the human opponent (MAXimize value) and at the same time the worst possible variants for the computer (MINImize value)? In other words, at the end, how can the computer play its best move taking into account the worst scenario, which we will have only if the human opponent plays his best move?

EXERCISE: Try to think of the above on your own, WITHOUT reading online about the MiniMax algorithm. First of all – who knows? – you might think of something better! Secondly, one only masters knowledge when he has tried on his own and failed to acquire it. Having things ready for you simply does not lead to wisdom.

The solution to the above problem is the MinMax algorithm. The current version of Huo Chess utilizes it, so you can read there how it works. However note that you can never learn anything by simply copying the program of someone else. Unless you write the program on your own, make mistakes, spend countless nights and days pondering upon them, you will never truly develop anything.

Make sure you check the relevant Huo Chess C# tutorials which refer in more depth to the algorithm (which stays the same regardless of the programming language you use of course).

More updates coming soon!

Until next time…

Happy coding!

Programming for kids: Developing a chess program in BASIC – Part 3

Interested in chess programming? You are interested in philosophy too! What is thinking? Can AI be conscious? What does it mean to do something? Can the AI understand that it does play chess? Explore the AI related articles in Harmonia Philosophica and understand why you are already a philosopher!

Programming for kids – How to program a chess program in BASIC

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In the previous lesson we managed to create the function which checks the legality of a move. With that we managed to check the move entered by the user and present it to the screen.

What is next?

To make the computer think of an answer!

In essence this is easy! (the difficult part is to make the computer think of a really good move, that we will tackle later on)

What we will need besides the SUB which checks the legality of the move (ElegxosNomimotitas) is a SUB which counts the score of any given position in the chessboard. This will allow us to evaluate all the possible moves, so that the computer can select the best one.

This is done by the code below, which simply scans the chessboard for pieces and adds or subtracts the value of each piece it founds from the total score of the chessboard.

SUB countScore

positionScore = 0

FOR I = 1 TO 8
    FOR J = 1 TO 8

        IF chessboard$(I, J) = "wpawn" THEN positionScore = positionScore + 1
        IF chessboard$(I, J) = "wrook" THEN positionScore = positionScore + 5
        IF chessboard$(I, J) = "wknight" THEN positionScore = positionScore + 3
        IF chessboard$(I, J) = "wbishop" THEN positionScore = positionScore + 3
        IF chessboard$(I, J) = "wqueen" THEN positionScore = positionScore + 9
        IF chessboard$(I, J) = "wking" THEN positionScore = positionScore + 100

        IF chessboard$(I, J) = "bpawn" THEN positionScore = positionScore - 1
        IF chessboard$(I, J) = "brook" THEN positionScore = positionScore - 5
        IF chessboard$(I, J) = "bknight" THEN positionScore = positionScore - 3
        IF chessboard$(I, J) = "bbishop" THEN positionScore = positionScore - 3
        IF chessboard$(I, J) = "bqueen" THEN positionScore = positionScore - 9
        IF chessboard$(I, J) = "bking" THEN positionScore = positionScore - 100

    NEXT J
NEXT I

END SUB

Easy? Pretty much. All that is needed is a nested FOR loop (read in QB64 here how that works). Nothing more.

Now that we have that, let’s move on to the main course.

The ComputerMove SUB, which – you guessed right – makes the computer perform a move!

In essence, the steps needed are pretty simple:

Scan the chessboard.

If you find a piece of the computer, then…

Scan all possible moves of that piece to all squares of the chessboard.

For every possible move, check the legality of that move.

If the move is legal, then make it!

Check the score of the move.

If the score is best than the current best move (in the beginning there obviously no current best move), then this is the current best move!

After you have examined all the possible moves, do the current best move

Simple isn’t it?

How is the scanning of the chessboard and the checking of all possible moves performed? With four nested FOR loops.

Check the code below. It simple scans all the chessboard with the FOR loops of I and J and then, if it finds a piece which belongs to the computer, it scans all possible destination squares with the FOR loops of ii and jj.

How do we determine is the piece we found is one of the computer’s pieces? We compare the first letter of the piece (which would be ‘w’ or ‘b’ for white and black pieces) with the color of the player. If for example the color of the player is ‘w’ (for white) and we encounter a piece ‘brook’, then this is a piece of the computer since it is black – i.e. opposite than the color of the player.

'Scan the chessboard...
FOR I = 1 TO 8
    FOR J = 1 TO 8

        'If you find a piece of the computer...
        IF ((MID$(chessboard$(I, J), 1, 1) = "w" AND playerColor$ = "b") OR (MID$(chessboard$(I, J), 1, 1) = "b" AND playerColor$ = "w")) THEN

            'Scan all possible destination squares...
            FOR ii = 1 TO 8
                FOR jj = 1 TO 8

                    startingColumn = I
                    startingRank = J
                    finishingColumn = ii
                    finishingRank = jj

                    MovingPiece$ = chessboard$(I, J)
                    ProsorinoKommati$ = chessboard$(ii, jj)

                    'Check legality of the move entered
                    CALL ElegxosNomimotitas(chessboard$(), 0, startingColumn, startingRank, finishingColumn, finishingRank, MovingPiece$)

                    'If move is legal, then do the move and present it in the chessbooard
                    IF Nomimotita = 1 THEN

                        'Do the move
                        chessboard$(finishingColumn, finishingRank) = chessboard$(startingColumn, startingRank)
                        chessboard$(startingColumn, startingRank) = ""

                        'Count the score of the move
                        CALL countScore

                        'If the score is better than the existing best score, then this is the best move now (and the best score)
                        IF ((playerColor$ = "b" AND positionScore >= bestPositionScore) OR (playerColor$ = "w" AND positionScore <= bestPositionScore)) THEN
                            bestStartingRank = startingRank
                            bestStartingColumn = startingColumn
                            bestFinishingRank = finishingRank
                            bestFinishingColumn = finishingColumn
                            bestPositionScore = positionScore
                        END IF

                    END IF

                    'Undo the move
                    chessboard$(startingColumn, startingRank) = MovingPiece$
                    chessboard$(finishingColumn, finishingRank) = ProsorinoKommati$

                NEXT jj
            NEXT ii

        END IF

    NEXT J
NEXT I

If the move analyzed is legal (the ElegxosNomimotitas SUB is called to determine that) then the move is performed. The score of the position resulting after that is counted (the CountScore SUB is called for that). If the score is better than the current ‘best score’ (the initial best score is zero of course) then this move is registered as best move.

After the scanning is complete, we simply perform the best move!

'Do the best move found
chessboard$(bestFinishingColumn, bestFinishingRank) = chessboard$(bestStartingColumn, bestStartingRank)
chessboard$(bestStartingColumn, bestStartingRank) = ""
CLS
CALL drawBoard

Easy? Yes!

Happy coding!

Read the full program here. Copy and paste the code in your QBasic interpreter/ compiler to see it and compile it.

IMPORTANT NOTES

  • The code is draft and errors might exist. Make sure to also check Huo Chess in C# (can be found in Harmonia Philosophica here).

Next lesson: How to improve the game of the computer.

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