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Joseph Petitti —

Using AI to make and play Mario Levels

This term I decided to branch out from my usual computer science courseload and try out a class from a very similar department: Interactive Media and Game Development. To that end I took a class called "Artificial Intelligence in Interactive Media and Games," which turned out to be a lot of fun. (It's also nice that it counts for CS credit).

One of the projects I did for the class was to use various AI techniques to build a generator for Super Mario Bros. levels, and an agent that can appear human while playing them. A partner and I used Ahmed Khalifa's Mario AI framework to implement these systems in Java, and they both ended up working pretty well.

Phase 1: the level generator

We used a Markov chain model to generate new Mario levels from inputs of existing ones, both from the games and created by other generators. It reads in level files from a specified directory, parses each vertical column of blocks into a "slice," and adds it to a Markov chain transition table. Basically each unique slice has a list of which slices follow it, and the probability that it is followed by that specific slice.

To generate a level, it picks a random slice to start with, then picks another to follow it based on the transition table, and so on. This continues until the generator selects a slice with a flag in it or reaches the desired level length.

Overall, the Markov chain-based generator worked decently, and managed to make some nice-looking levels when given the original Super Mario Bros. maps as input:

A Mario level generated by our algorithm

The main weakness of this type of level generator is one of the main properties of Markov chains: memorylessness. The decision of which slice to select is based only on the previous slice. This means there's no way for the generator to tell if it's making pits that are too wide to jump across or a long row of boring nothingness.

Phase 2: the AI agent

For this phase of the project we tried to create an agent that could successfully navigate Mario levels while convincing viewers that a human was playing. Although it would be easy to implement an A* algorithm that always makes it to the flag, we wanted it to look like a flawed human was behind the controller, which is a much more difficult task. We implemented a decision-tree AI system to do this, since this type of model can provide complex behavior while being easy to understand and debug.

We created a basic tree data structure with two types of nodes—action and question nodes. At a question node, the agent makes some kind of decision based on information it has about the environment. For example, whether Mario is standing next to a pit or not. Action nodes are the leaves of the tree, representing the action taken after a series of decisions. For example, if Mario is standing on the ground and there is an enemy to the right he might take the action "jump right."

Using basic decisions and actions as building blocks we constructed a decision tree that would be executed each game step to get the next action for our agent to take. We incrementally tweaked the shape and nodes of the tree until we got an agent that looks almost human while playing a level:

Our agent ended up being not terrible, but it probably could have been a lot better. It turns out that decision trees aren't a great choice for this type of problem for the same reason Markov chains aren't the best at designing levels: they don't have any memory. Because the same tree has to be run every game step, it's difficult to do complex actions like long jumps because the tree doesn't hold a global state.

Overall, it was still a fun project even though the design decisions we chose turned out to not be the most optimal. I learned a lot about procedural generation that I would later put to use in a JavaScript rogue-lite game I'm working on, and had fun experimenting with different AI techniques.