A Bayesian Ensemble Regression Framework on the Angry Birds Game

TL;DR

This paper introduces a Bayesian ensemble regression framework for the Angry Birds game, utilizing an efficient tree-based encoding of game states to improve decision-making through multiple regression models.

Contribution

It presents a novel multi-model Bayesian regression approach combined with an efficient tree structure for game state encoding, enhancing game-playing performance.

Findings

Demonstrates improved game level performance

Shows the effectiveness of the ensemble regression approach

Validates the method through comparative experiments

Abstract

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.