A CBR Approach to the Angry Birds Game

In this paper, we present a CBR approach for implementing an agent playing the well-known Angry Birds game. We adopt a preference-based procedure for constructing the case base, collecting experience from a random agent that continually explores the problemsolution space and improves the quality of already found solutions. As the retrieve phase involves finding a game scene similar to a given one, we develop a measure to assess the dissimilarity between two game scenes, which is based on solving appropriate linear assignment problems. A comparison of our agent with state-of-the-art computer programs shows promising results.