A Bayesian Ensemble Regression Framework on the Angry Birds Game
  An ensemble inference mechanism is proposed on the Angry Birds domain. It is
based on an efficient tree structure for encoding and representing game
screenshots, where it exploits its enhanced modeling capability. This has the
advantage to establish an informative feature space and modify the task of game
playing to a regression analysis problem. To this direction, we assume that
each type of object material and bird pair has its own Bayesian linear
regression model. In this way, a multi-model regression framework is designed
that simultaneously calculates the conditional expectations of several objects
and makes a target decision through an ensemble of regression models. Learning
procedure is performed according to an online estimation strategy for the model
parameters. We provide comparative experimental results on several game levels
that empirically illustrate the efficiency of the proposed methodology.
