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Angry Birds has been a popular game throughout the world since 2009. The goal of the game is to destroy all the pigs and as many obstacles as possible using a limited number of birds. Since the game environment is subject to change tremendously after each shot, a deterministic planning model is very likely to fail. In this paper, we integrate deliberately planning and acting for Angry Birds with refinement methods. Specifically, we design a refinement acting engine (RAE) based on ARP-interleave with Sequential Refinement Planning Engine (SeRPE). In addition, we implement greedy algorithm, Depth First Forward Search (DFFS) and A* algorithm to perform the actor's deliberation functions. Eventually, we evaluate our agent to solve the web version of Angry Birds in Chrome using the client-server platform provided by the IJCAI 2015 AI Birds Competition. In our experiments, we find out that our agent using SeRPE with A* algorithm greatly outperforms the agent using greedy algorithm or forward search without SeRPE. In this way, we prove the significance of refinement methods for planning in practice.


R1: Very impressive work, Ruofei. The video is superb.

Technical Report

In CMSC 722 AI Planning, Spring 2015


Presented on CMSC 722 AI Planning, Spring 2015


    title={Deliberately Planning and Acting for Angry Birds with Refinement Methods},
    author={Du, Ruofei and Gao, Zebao and Xu, Zheng},


Du, R., Gao, Z., Xu, Z. (2015, May). Deliberately Planning and Acting for Angry Birds with Refinement Methods.