Teleology (a.k.a – what is the purpose of this post?)
Recently, I finished an artificial intelligence project that involved implementing the Minimax and Alpha-Beta pruning algorithms in Python.
These algorithms are standard and useful ways to optimize decision making for an AI-agent, and they are fairly straightforward to implement.
I haven’t seen any actual working implementations of these using Python yet, however. So I’m posting my code as an example for future programmers to improve & expand upon.
It’s also useful to see a working implementation of abstract algorithms sometimes, when you’re seeking greater intuition about how they work in practice.
My hope is that this post provides you with some of that intuition, should you need it–and that it does so at an accelerated pace.
Ontology (a.k.a – how does this abstract idea __actually work__, in reality?)
Let’s start with Minimax itself.
Assumptions: This code assumes you have already built a game tree relevant to your problem, and now your task is to parse it. If you haven’t yet built a game tree, that will be the first step in this process. I have a previous post about how I did it for my own problem, and you can use that as a starting point. But keep in mind that YMMV.
My implementation looks like this:
How-to: To use this code, create a new instance of the Minimax object, and pass in your GameTree object. This code should work on any GameTree object that has fields for: 1) child nodes; 2) value. (That is, unless I made an error, which of course, is very possible)
After the Minimax object is instantiated, run the minimax() function, and you will see a trace of the program’s output, as the algorithm evaluates each node in turn, before choosing the best possible option.
What you’ll notice: Minimax needs to evaluate **every single node** in your tree. For a small tree, that’s okay. But for a huge AI problem with millions of possible states to evaluate (think: Chess, Go, etc.), this isn’t practical.
How we solve: To solve the problem of looking at every single node, we can implement a pruning improvement to Minimax, called Alpha-Beta.
Alpha-Beta Pruning Improvement
Essentially, Alpha-Beta pruning works keeping track of the best/worst values seen as the algorithm traverses the tree.
Then, if ever we get to a node with a child who has a higher/lower value which would disqualify it as an option–we just skip ahead.
Rather than going into a theoretical discussion of WHY Alpha-Beta works, this post is focused on the HOW. For me, it’s easier to see the how and work backwards to why. So here’s the quick and dirty implementation.
How-to: This algorithm works the same as Minimax. Instantiate a new object with your GameTree as an argument, and then call alpha_beta_search().
What you’ll notice: Alpha-Beta pruning will always give us the same result as Minimax (if called on the same input), but it will require evaluating far fewer nodes. Tracing through the code will illustrate why.
Phenomenology (a.k.a – how will I perceive this, myself?)
This isn’t the most robust implementation of either algorithm (in fact it’s deficient in many ways), so I wouldn’t recommend it for industrial use.
However, this code should simply illustrate how each algorithm works, and it will provide output you can trace through and compare against–as long as you are able to construct the GameTree for your problem.
From there, it’s only a matter of time until you’ll understand it intuitively. This is one of those things that took a little while for me to grasp–so hopefully having a clear example will help others get there more quickly. Good luck.