Exploitive Bot Development. Step 1

As mentioned previously, an exploitive bot needs to learn how the opposition plays and develop counter strategies to their style. The first thing we need to do to achieve this is gather information on our opposition. There is already software out there to do this for you such and Hold'em Manager, Poker Tracker and Poker Office, you can then tap into their databases. The other option is to create your own database and write a hand importer yourself, the only advantages of this being it's more flexible and you can use a more powerful database engine and tools such as Microsoft Sql Server.

Once you've done this you need hand histories to import, you may have many hand histories of your own, but I'd recommend starting out at the lowest stakes, so unless you've been playing $2 games for a while you're unlikely to have enough. There are various sites online where you can buy hand histories, and several places you can pick up obfuscated hand histories for free.

Once you've got all your hand histories and started talking to your database you can start the process of working with the data. There are two approaches here, a statistics based approach, or a machine learning approach.

The statistics based approach is simplest, it basically says if player x bets in this situation 15% of the time, then we'll assume they're doing this with the top 15% of hands, it's pretty crude, but effective.

With the machine learning approach we're starting to get into the world of Skynet. I'm not going to go into too much detail here, as there is plenty of information on Wikipedia and for more poker specific articles you can take a look at PokerAI.

The general idea behind the machine learning approach is that the computer is going to learn how other players play based on their statistics, and we can use this information to predict how they'll act is certain situations and respond to that. Clever stuff hey?! Machine learning is still very basic however. Artificial neural networks, for example, try to mimic the human brain's method of reinforcement learning. But... the human brain has an estimated 100 billion neurons and is effectively running millions of parallel CPU cores, we'll be working with networks with a number of neurones that you can count on your hands and toes, and unless you have a lot of money, you're not going to be working with millions on parallel CPUs either. So it's a bit like trying to teach poker to a toddler, sometimes they just don't understand!

Where do I start?

I'm not going to be dealing with the poker bot basics in this blog. If you've got it into your head that you want to have a shot a writing one, but don't know where to start, then take a look at this Coding The Wheel article. That'll introduce you to the concepts of interfacing and interacting with the poker client of your choice.

In this blog I'm going to be picking up from where I'm currently at, there will be various tips and tricks that developers may find useful as well as code snippets from what I'm working on and an overview of how my bot and AI work.