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A Bayesian Ensemble Regression Framework on the Angry Birds Game

2015; Institute of Electrical and Electronics Engineers; Volume: 8; Issue: 2 Linguagem: Inglês

10.1109/tciaig.2015.2494679

1943-0698

Nikolaos Tziortziotis, Georgios Papagiannis, Konstantinos Blekas,

Advanced Bandit Algorithms Research

In this paper, we introduce AngryBER, an intelligent agent architecture on the Angry Birds domain that employs a Bayesian ensemble inference mechanism to promote decision-making abilities. It is based on an efficient tree-like structure for encoding and representing game screenshots, where it exploits its enhanced modeling capabilities. This has the advantage to establish an informative feature space and translate the task of game playing into a regression analysis problem. A Bayesian ensemble regression framework is presented by considering that every combination of objects' material and bird type has its own regression model. We address the problem of action selection as a multiarmed bandit problem, where the upper confidence bound (UCB) strategy has been used. An efficient online learning procedure has been also developed for training the regression models. We have evaluated the proposed methodology on several game levels, and compared its performance with published results of all agents that participated in the 2013 and 2014 Angry Birds AI competitions. The superiority of the new method is readily deduced by inspecting the reported results.