A Bayesian Ensemble Regression Framework on the Angry Birds Game

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. The 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.

The 2017 AIBIRDS Level Generation Competition

“…The more transparent participating teams are with their breakthroughs, the more successful future agents will be. Several previous competition entrants have already published their research and agent designs in academic conference or journal papers [8], [9], [10], [11], [12], [13], [14], and we hope that future participants will continue doing this.…”

“…The eventual goal of this competition is to design agents that can play new levels as well as, or better than, the best human players. Many of the previous agents that have participated in this competition employed a variety of techniques, including qualitative reasoning [8], internal simulation analysis [9], [10], logic programming [11], heuristics [12], Bayesian inferences [13], [14], and structural analysis [15].…”

The computational complexity of Angry Birds

“…internal simulation analysis [9,10], logic programming [11], heuristics [12], Bayesian inferences [13,14], and structural analysis [15]. Despite many different attempts over the past five years the problem is still largely unsolved, with AI approaches far from human-level performance.…”

Using Restart Heuristics to Improve Agent Performance in Angry Birds

“…Previous agents in the AIBIRDS competition have implemented a variety of techniques such as Bayesian reinforcement learning [5], qualitative reasoning [2], heuristics [4], internal simulations [10] or a combination of other agents [12].…”