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An ensemble inference mechanism is proposed on the Angry Birds domain. It isbased on an efficient tree structure for encoding and representing gamescreenshots, where it exploits its enhanced modeling capability. This has theadvantage to establish an informative feature space and modify the task of gameplaying to a regression analysis problem. To this direction, we assume thateach type of object material and bird pair has its own Bayesian linearregression model. In this way, a multi-model regression framework is designedthat simultaneously calculates the conditional expectations of several objectsand makes a target decision through an ensemble of regression models. Learningprocedure is performed according to an online estimation strategy for the modelparameters. We provide comparative experimental results on several game levelsthat empirically illustrate the efficiency of the proposed methodology.

A Bayesian Ensemble Regression Framework on the Angry Birds Game