Online Poker Bots - Threat or Not?

 by: Harrison Elliot

Introduction

Online games from chess and backgammon to 1st person shooters are rife with people who use computer assisted play or computer robots - even when there is no money at stake. With the appropriate program any player is able to play at a world championship level ruining the game for honest players. What makes online poker different, given that there are huge amounts of money at stake?

In this article I will explain how I developed a poker bot and what I learnt from this experience. My conclusion is that although it is possible to construct a poker playing bot the threat from poker bots to the online poker player is extremely small to non-existent.

"Games" Theory

For interest and college courses I had previously written computer playing programs or bots for games including chess, Connect 4, Othello, backgammon, bridge and various others. For games such as Connect 4, Othello, chess and backgammon where all players have the same available information about the game state, the theory on how to construct expert bots is well known. Deep search techniques, looking many moves ahead, are used for games such as Othello and chess. Recently (10 years ago) it was discovered neural networks could be taught to play backgammon better than almost any human player. Games such as poker and bridge contain hidden information where the players can see their own hand but not that of the other players. The published theory behind writing expert computer bots for these incomplete information games is decades behind the complete information games and there are doubts techniques will be developed so that computers can play at expert or world championship level. At present the best techniques for these incomplete information games seem to involve some form of simulation and opponent modelling.

Anatomy of An Online Poker Bot

There are 3 basics parts to a poker bot.

1) Data Gathering - observing the game state and history

2) Data Processing - using the information from the data gathered to determine whether to fold, call or raise.

3) Output - Pressing the appropriate button on the poker room client.

My Poker Bot

My program was written in early 2004 with Microsoft .Net C++ and was developed to play at one online room only. For the technically minded the program relied heavily on MFC and the Win32 API.

1) Data Gathering

My bot gathered information about game state and history from online poker tables by taking repeated screenshots and analysing the image. To begin with I just observed games, taking screenshots automatically so I could gather data on the position of the cards, chips and button. By determining the color of a certain few pixels I was able to gather all this information about the state of the game.

Eventually I was able to gather data from multiple poker tables (4 at a time) by repeatedly bringing each window to the foreground and taking a screenshot. From this screenshot I was able to determine my cards, board cards, button position, who was left in the hand, pot size and player bet sizes.

2) Data processing

This is the component that eventually bought my poker bot project to an end, unable to develop a strong enough strategy to win consistently. I tried various rules based, neural net and simulation techniques. At best my bot was able to make a very small profit at $1/2 and $2/$4 limit hold’em, but nowhere near the thousands of dollars a week I envisioned earning when I started the project. In the end it just wasn’t worth my time to continue to put resources into developing my poker bot further.

3) Output

This was the easiest component to write. This involved programmatically moving the mouse pointer to the appropriate screen co-ordinates and then sending a mouse down/mouse up command signalling a left-click. I did give consideration to adding the ability for the bot to use chat but never progressed that far.

Conclusion

Although you might run into a poker playing bot whilst playing online the odds are it plays very poorly. At any level of play you are much more likely to run into an expert human player than an expert computer player.