A. Narayan-Chen, L. Xu & J. Shavlik (2013).
An Empirical Evaluation of Machine Learning Approaches for Angry Birds. IJCAI Symposium on AI in Angry Birds.
Poster (PPTX). Slides (PPTX).

This publication is available in PDF.

The slides for this publication are available in Microsoft PowerPoint.

Abstract:

Angry Birds is a popular video game in which players shoot birds at pigs and other objects. Because of complexities in Angry Birds, such as continuously-valued features, sequential decision making, and the inherent randomness of the physics engine, learning to play Angry Birds intelligently presents a difficult challenge for machine learning. We describe how we used the Weighted Majority Algorithm and Naive Bayesian Networks to learn how to judge possible shots. A major goal of ours is to design an approach that learns the general task of playing Angry Birds rather than learning how to play specific levels. A key aspect of our design is that the features provided to the learning algorithms are a function of the local neighborhood of a shot's expected impact point. To judge generality we evaluate the learning algorithms on game levels not seen during training. Our empirical study shows our learning approaches can play statistically significantly better than a baseline system provided by the organizers of the Angry Birds competition.

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