how computers play chess

How Computers Play Chess

Wednesday, September 25, 13

A bit about meCarlos Justiniano ([email protected])

Started playing chess at age 7, reached master strength in (slow) online play.

Project Manager / Team Lead on the Chessmaster series.

Founded ChessBrain - a distributed computing project which earned a Guinness 2005 world record as the world’s largest chess computer.


mailto:[email protected]

mailto:[email protected]

Overview

In this talk we’ll look at how computers play chess.

We’ll begin with a brief history of chess.

Then we’ll look at the core modules which make up all chess programs.

We’ll end with a look inside an actual chess program.


Chess History

Chess is well over 1000 years old.

Believed to have originated in India before spreading to Persia.

The style of chess we play today took form in Europe during the 15th century.

At about the that time chess books were written.


Chess History

During the 18th century, Philidor wrote The Analysis of Chess (L’analyse des échecs).

He was considered to be one of the World’s best chess players in his time.

François-André Danican Philidor


It’s been said that more books have been written about chess than for all other

games combined!


So why this fascination with Chess?

Long considered an intellectual game - the game of kings, queens and generals.

A game which embodies the struggles of common men to entire kingdoms.

A epic battle of life and death.


Fast forward to the future...


Chess TodayToday, chess is played around the globe. It’s estimated that out of 7 billion people about 700 million have played chess at one point in their lives.

Computers have surpassed the best human players.

Today competitive chess players use computers in order to prepare for their human opponents.


The creation has surpassed its creator

So, although we humans have had over 1000 years to invent, study and understand the finer points of chess - machines are now better at playing chess than we are!


Whoa! No way dude! How did this happen?


It didn’t happenover night

As early inventors created machines, public fascination grew for some of the more clever machines exhibiting human characteristics.

Early inventor, Al Jazari, described many such inventions in his book “The Book of Knowledge of Ingenious Mechanical Devices” which he wrote in 1206.

Surely, it was only a matter of time before machines could play chess.


The Turk, Chess Playing Automaton

Built in 1769 by Wolfgang von Kempelen.

The Turk beat many players but lost to the strongest players of the time, such as Philidor.

It won many more games than it lost and was a sensation throughout Europe.


The Turk, Chess Playing Automaton

Sadly, however, it was an elaborate hoax.

The Turk concealed a human chess master.

The world would not see a true chess playing machine for another 180 years.


Fast forward to the late 1930s

The creation of electronic computers began in the 1930s.

By the late 40s, computers were used as research and military tools in the US, England, Germany and the former USSR.

The ENIAC, which became operational in 1946, is considered to be the first general-purpose electronic

computer. Programmers Betty Jean Jennings (left) and Fran Bilas (right) are depicted here operating the

ENIAC's main control panel.


http://en.wikipedia.org/wiki/ENIAC

http://en.wikipedia.org/wiki/ENIAC

http://en.wikipedia.org/wiki/Betty_Jean_Jennings

http://en.wikipedia.org/wiki/Betty_Jean_Jennings

http://en.wikipedia.org/wiki/Fran_Bilas

http://en.wikipedia.org/wiki/Fran_Bilas

Early research papers in Computer Chess

In 1945, Konrad Zuse, the German pioneer of computer science first wrote about the possibility of creating a chess program.

Although Konrad developed one of the first electronic computers, the Zuse-1, there’s no record of him actually creating a chess program.


Early research papers in Computer Chess

Four years later, in 1949, Claude Shannon (a research scientist at Bell Labs) authored a seminal paper entitled “Programming a Computer for Playing Chess”.

Many of Shannon’s ideas are still in use today!


Turing takes things a few steps further

In 1951, British mathematician and early computer scientist, Alan Turing wrote about computer chess.

He later completed a one move chess analyzer called TUROCHAMP.

Turing’s chess analyzer didn’t run on an actual computer, but was rather a set of instructions he could execute by hand.


Turing takes things a few steps further

Turing simply calculated chess moves by looking ahead one move at a time and scoring them.

Although played on paper, this was the first program to play a complete game of chess.

Turing believed that games such as chess served as ideal models in which to study machine intelligence.


No worries!We’re not going to cover all of computer chess history.

Let’s speed things up a bit...


The 50s - 80sIn 1957, Chess programs using a 6x6 board instead of the 8x8 board began playing simple chess.

By 1957, the first program to play a game of chess was developed by Alex Bernstein in the US and one by programmers in Russia.

In 1961 a Russian chess program played a game against a human chess amateur. This was the first recorded game of man vs machine. Although the machine lost, it wouldn’t be long before the tables would turn.

Along with advances in main-frame and mini computers, chess programs also continued to improve during the 60s and 70s.

During the 70s and 80s, Joe Condon and Ken Thompson at Bell Labs created Belle, the first chess machine to reach master strength play. Side note: you may remember Ken Thompson as the creator of the UNIX operating system.


http://en.wikipedia.org/w/index.php?title=J._H._Condon&action=edit&redlink=1

http://en.wikipedia.org/w/index.php?title=J._H._Condon&action=edit&redlink=1

http://en.wikipedia.org/wiki/Ken_Thompson_(computer_programmer)

http://en.wikipedia.org/wiki/Ken_Thompson_(computer_programmer)

http://en.wikipedia.org/wiki/Bell_Labs

http://en.wikipedia.org/wiki/Bell_Labs

The 80s also ushered in low cost chess computers


The 80s and 90s

During the 80s we also started seeing chess programs for the early personal computers as well as dedicated chess machines for consumers.

The 90s were an exciting time in computer chess history as chess programs began challenging International Chess masters and later Grandmasters.

This progress was fueled by faster computers, improvements in software and advances in computing science.


IBM’s Deep BlueBy 1997 IBM’s Deep Blue chess machine beat then World Champion, Garry Kasparov by two wins against one win and three draws.

This marked the first time in human history that a machine had ever defeated a World Champion.


http://en.wikipedia.org/wiki/Garry_Kasparov




Early 2000sDeep Blue was a highly specialized machine designed to play chess. However, PCs were also getting better!

In the early 2000s there were three high profile matches between world-class chess players and PCs.

Keep in mind that these games where played against “gamer-class” PCs and not large mini and super computers. Not bad for machines less powerful than Deep Blue.

In 2002 Vladimir Kramnik and Deep Fritz competed an eight game match ending in a draw.

In 2003, Garry Kasparov played Junior, The match ended a draw with 3–3.

Later that year, Kasparov played X3D Junior in a match also ending in a draw.


In 2005, Human chess dominance ends

In 2005, Hydra (a specialized Chess machine using 64 processors) defeated the 6th ranked player in the world, Michael Adams 5½ to ½ in a six-game match.

In 2006 undisputed World Champion Vladimir Kramnik played Deep Fritz and lost by a score of 2 to 4.

Today chess programs running on our mobile phones play better than all but the world’s top human players.


http://en.wikipedia.org/wiki/Michael_Adams_(chess_player)

http://en.wikipedia.org/wiki/Michael_Adams_(chess_player)

http://en.wikipedia.org/wiki/Vladimir_Kramnik

http://en.wikipedia.org/wiki/Vladimir_Kramnik

Today, all competitive chess players train using machines - most have no

chance of winning against the machines in actual tournament play


Speed in perspectiveIn the 1980s a microcomputer could execute just over 2 million instructions per second, by the 90s they were executing well over 50 million instructions per second.

Today, the processors in our tablets and phone are capable of executing over a billion instructions per second.

Advances in computer science and software tools have also helped considerably, but advances in raw processing speed can’t be ignored.


Thinking Games

To understand how computers play chess it’s important to understand how they play simpler games.


Thinking GamesWe’ve all played simple games like tic-tac-toe and the 15 tile sliding game.

These games require us to consider which moves will bring us closer to winning.


Thinking GamesThe process of thinking about how to win involves employing strategies which bring us closer to achieving a solution.

All games have strategies. In Monopoly it’s important to obtain high priced properties, in Reversi/Othello controlling the corner square is vital.

For computer programs these strategies are described using Heuristics and Algorithms.


Heuristics and Algorithms

In computer science the term Heuristic refers to a method of solving a problem by getting closer to a solution or end goal.

In contrast the term algorithm refers to a method (approach) of solving a particular problem.

It’s common for Heuristics to involve one or more algorithms.


Most non-trivial games can’t be realistically solved

with an algorithm - largely because it would

take too damn long


Solving ChessTake chess for example: there exists a way of finding the best move at the start of the game.

However, it involves looking at all possible moves and the replies to each of those moves -- followed by replies to those moves, and so on until an end is reached.

Not a bad plan if it were not for the shear number of possible chess moves in a given game.


If we assume an average chess game of 40 moves, there are...

10^120 possible moves, or

10,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000

At a calculation speed of 1/1000 of a second for each move - it would take a computer 10^90 years to make the first move.


10^120 is a really really (yes really) big number!

Fictional side note: It turns out that it’s considerably larger than the seven million years required for Deep Thought to arrive at the answer: 42


Can’t win by brute forceBrute force refers to an approach which explores all possibilities.

Complex intelligent games can’t be solved by brute force.

So moves have to be selectively chosen.

This realization is what has driven advances in how intelligent games are built.


Evaluating choicesSo if choices have to be evaluated then we need a way of performing an evaluation.

In the field of intelligent games this is referred to as an evaluation function.

The function says: Given a known move return a score.

The best move in a list of choices is the move which scored the highest.


How a machine sees

Before a chess program can evaluate a move, we must first teach it how to represent a chess position.

This is unsurprisingly known as “Board representation”.


How a machine sees

A chess board can be represented as a numbered list of squares.


How a machine seesThe chess pieces themselves can be represented using the numbers -1 through 6.

Piece colors are indicated using positive and negative numbers.


How a machine seesThis is by no means the only way to represent a board and pieces.

Other methods exist with names such as “bitboards” and “Forsyth-Edwards Notation”.

In this talk we’ll stick with our earlier method.


How a machine sees

Following our earlier example, it’s necessary to expand upon our simplistic board representation in order to detect chess moves which fall out of bounds.

Essentially we need to encode the board’s boundaries.


How a machine sees

This results in a larger board which includes our actual chess board within.

A value of -99 is used to represent out of bound areas.


How a machine sees

Thus our numbered list of squares also changes to allow for our expanded board.


How a machine sees

Next, we need to help the machine understand what it sees.

We need to teach it how each piece moves, and finally chess rules which further constrain legal moves.

For example, the King chess piece can’t move into a square which is already under attack by the opposing side.


How a machine sees

Let’s consider the Knight chess piece.

It moves in an L shaped path: two squares in a horizontal or vertical direction and then one square to either side.


How a machine seesWith a black Knight on square 78 we can see that it can move to squares 53, 55, 64, 68, 88, 92, 101, and 103.

We can encode the Knight’s movements as the difference between the square it can move to and the square its currently on.


How a machine seesA movement to square 53 involves a subtraction 53-78=-25 So we encode that move as -25.

Moving to square 101 involves 101-78=23 so we encode that as +23.

This encoding works no matter where we place the Knight.


How a machine seesLets place our Knight in the lower corner of the board.

Notice how most of its moves fall out of bounds.

This is why we use a larger board: in order to detect piece moves which aren’t valid.


How a machine seesA chess programmer proceeds to encode the offset differences for each piece.

This algorithm is known as a legal move generator.

With a list of legal moves the program can then run the evaluation function against each resulting move to begin to isolate a best move.


Inside an evaluation function

In chess an evaluation function would essentially weigh various desirable characteristics in a chess position and return a score.

This is one of the earliest methods used in computer chess.



A simple evaluation function would ask questions like: “how many moves do I have available?”, “is my King safe?”.

Evaluation functions are typically implemented using a weighted sum model. This approach assigns relative values to various chess factors to arrive at a weighed score.



Before we can assign values in a weighed sum function we have to agree on a basic unit of measurement.



In computer chess a pawn is assigned the value of 100, a knight is assigned 300, a bishop 350, a rook 500 and a Queen is valued at 900. A king is assigned a large number because capturing the king marks the end of the game.



Each factor of an evaluation function is assigned a value relative to a centipawn, that's 1/100 of a pawn. For each desired factor, a chess programmer asks, "how much of a pawn is that factor worth?".



Evaluation functions also contain a measure of material balance - that is, by a show of remaining pieces - who is winning?. This is determined by adding up the value of each side’s pieces, which are already measured in centipawns. So a knight and two pawns is equal to 500 or equal in value to a Rook.



Here is another example: A chess game begins with each side having two bishops. It's considered advantageous to retain both bishops for as long as possible.

So an evaluation function might value the present of both bishops as equal to half a centipawn or 50. Once a player no longer has both bishops an evaluation function would cease to add 50 to its overall score.



Many factors go into an evaluation function, such as king safety (can the king be attacked, is it safe?) and Piece Mobility (how many moves are available to a given piece) greater mobility often equates to more options or opportunities.

The presence or absence of various factors is what determines how a position is scored.



Here is a really crude evaluation function where Queen=900, Rook=500, Bishop=350, Knight=300, Pawn=100.

f below is our evaluation function, the parameter p is the position to be evaluated, M is a measure of mobility.

f(p) = QueenWeight X (Qw-Qb) + RookWeight X (Rw - Rb) + BishopWeight X (Bw-Bb) + KnightWeight X (Nw-Nb) + (Pw-Pb)+ 0.1 X (Mw-Mb)



Simpler evaluation functions model how beginners see chess positions, while more complex evaluation functions model how very strong players see a position.

Complex evaluation functions take more time to execute than simpler ones.

Thus, other algorithms need to be employed to speed up the selection process.


Searching for a best move

So now that we’ve seen how a chess set (position) can be represented and how a it can be evaluated - we’re ready to consider how good moves can be found.


Look ahead

In order for a chess program to decide on a best move it must also take into account its opponents move - followed by its own replies and so on.

This is known as looking ahead. Good chess players look ahead a few moves while great chess players have been known to look ahead a dozen or so moves.


Keeping track of evaluations

We’ve seen how computers represent chess positions and how they evaluate them - but how do they keep track of what they’ve evaluated?

Enter: game trees a tool used to graph positions (nodes) and moves (vertices) and their relative evaluations.


When inverted a game tree appears more like a natural tree with a trunk, branches and leaves moving upward.


Game TreesIn computer science a tree is also known as a data structure.

Common data structures include arrays and hash tables (also known as dictionary or associative arrays).

Trees can be implemented as array of nodes - where each node contains both a pointer to its parent and an array with sibling nodes.


Game Trees

Game Trees, like other data structure, are useful for more than just storing data.

Algorithms typically operate on data structures.

For example, a sorted array may contain a list of places and an algorithm (binary search) might be used to find a specific location.


Game Trees and Search Algorithms

A game tree is built using a legal move generator and nodes are evaluated using an evaluation function - which was described earlier.

Search algorithms navigate the game tree while looking at the score left by an evaluation function.

As a search algorithm visits a node (position) it may further update other node values along the way.


Search Algorithms

Over the years many search algorithms have been devised.


Search AlgorithmsThe Minimax algorithm was the first search method used in computer chess. MAX values (positive) are assigned to moves for the first player, while MIN values are assigned to the moves of the second player. The game tree is filled with MIN and MAX values and ordered so that any given node contains the MIN value or MAX value of the best replies below it.

In this way a path to the best (highest scoring) move is identified.


Search AlgorithmsAlpha-Beta Pruning is a search algorithm which improves upon the Minimax algorithm by reducing the number of nodes which need to be evaluated.

It does this by discarding a move branch when it’s proven to be worse than one already identified.

We humans do this when we consider a choice which is so bad that we stop considering it and move on to more promising options.


Alpha beta pruning on a minimax tree


Improved Alpha/Beta

Over the years, many improvements have been made to the Alpha Beta Pruning algorithm.

If you’re interested, checkout NegaScout, PVS and MTF(f).


Key componentswe’ve covered

Board representation / game state: How to represent a given position.

Move Generation: Given a position, determine all of the legal moves.


Key componentswe’ve covered

Static Evaluation: How to assess a given position based on various factors.

Search: How to locate the best move in a game tree of chess positions.


That’s how computers play chess

The areas we’ve covered should give you a sense of how computers play chess.

Naturally this talk is a gross oversimplification but with additional research into the ideas we’ve covered most talented programmers should be able to build their own chess program.


Ideas in actionLet’s take a brief look at an actual chess program...

There are hundreds of chess programs available on the web. For this talk I choose GarboChess by Gary Linscott.

GarboChess is written in simple JavaScript and can run locally on your computer.


Now you know how computers play chess!

The ideas we’ve looked at apply to a wide range of games.

There are many sites online which further elaborate on the materials we’ve covered.


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