ML in Games – Part 5

5.0 Procedural Content Generation & Machine Learning

I’m hoping to find some interesting things here. Going into my final year, I am working on my own senior project that involved procedural content and story generation. I’m hoping that some of the ideas of machine learning may carry over into my own project. The term ‘content’ is rather vague, but for the purposes for this research, I will be looking at level/environment generation along with story/plot generation.

5.1 – Novelty Evaluation

Silvester explores some interesting ideas about how to evaluate the novelty of procedural level generation on various levels in his paper “Using Novelty Search to Generate Video Game Levels”. He discusses how we can evaluate generated content for it’s novelty or evaluate novelty based on the sequence of actions required to complete a level (“Using Novelty Search to Generate Video Game Levels”). Although his methods do not involve machine learning, I believe there is great potential here for integrating his ideas with a  learning system. Perhaps we could solve some of the issues that he mentions, such as having the level discernible from simple random generation by implementing a supervised learning system which is trained ahead of time to create levels that are both novel, and enjoyable based on novelty search methods and user feedback.

5.2 – Enjoyment Potential

Another fairly straightforward task for a machine learning system in procedural content generation is in the evaluation of the potential enjoyment associated with generated content. This also involves the possibility of training a system in the creation of enjoyable content, rather than just evaluating it afterwords. Pedersen, Togelius, and Yannkakis used supervised learning to train an algorithm which aims to generate enjoyable levels for the game Infinite Mario Bros. which is an open source version of the game Super Mario Bros. They created levels with variations in each of the features that their algorithm would control, and then collected surveys of a test population after playing each variation. This was used as training data for their system, which succeeded in identifying what lead to an enjoyable experience in a level, but they did express concerns with their original data set being too small to achieve truly great results (Pedersen, Christopher, Julian Togelius, and Georgios N. Yannakakis, 2010).

5.3 – Story Implementations

As mentioned previously in Part 2, and similar to the above, machine learning can be used to model a player as they play and feed this model into an interactive story engine to help it choose the arc of the story (Thue, Bultiko, Spetch, and Wasylishen, 2014).

Barber and Kudenko take a different approach to interactive storytelling by basing their generated narratives on dilemmas. Their system, entitled GADIN for Generator of Adaptive Dilemma-based Interactive Narratives, uses information about the story world (including characters), a list of possible actions, and dilemmas to generate story nodes for the viewer to navigate (Barber and Kudenko, 2009). The potential for machine learning here is quite exciting. Beyond incorporating a learning aspect which collects data about and models the player’s real attributes and traits for use in the GADIN, this could also be extended into the massively multiplayer online role-playing game (MMORPG) realm. Such a system could collect metrics about all players in the world and generate interactive dilemma-based narratives which are catered to and involve a larger group of players, or perhaps represents a player while they are away and gives out quests to other players based on the metrics collected about your player.


Silvester, Jim. “Using Novelty Search to Generate Video Game Levels”.

Pedersen, Christopher, Julian Togelius, and Georgios N. Yannakakis. “Modeling player experience for content creation.” Computational Intelligence and AI in Games, IEEE Transactions on 2.1 (2010): 54-67.

Thue, David, Vadim Bultiko, Macia Spetch, and Eric Wasylishen. “Interactive Storytelling: A Player Modelling Approach.” : n. pag. Association for the Advancement of Artificial Intelligence. Web. 23 June 2014.

Barber, Heather, and Daniel Kudenko. “Generation of adaptive Dilemma-Based interactive narratives.” Computational Intelligence and AI in Games, IEEE Transactions on 1.4 (2009): 309-326.

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