Akbaba—An Agent for the Angry Birds AI Challenge Based on Search and Simulation

Schiffer, Stefan; Jourenko, Maxim; Lakemeyer, Gerhard

doi:10.1109/tciaig.2015.2478703

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…One of the most prominent Angry Birds search-based agents is AKBABA (Schiffer, Jourenko, and Lakemeyer 2016), which uses search and simulation to find appropriate parameters for launching birds. Launching parameters are composed of a target within the scene, the bird's initial velocity, the bird's initial angle, and the point on the bird's trajectory at which its ability is triggered in case it has one.…”

Section: Related Workmentioning

confidence: 99%

Birds in Boots: Learning to Play Angry Birds with Policy-Guided Search

Pereira

Chaimowicz

Lelis

2021

AIIDE

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In this paper we present Birds in Boots (BiB), a system that uses a sampling-based search algorithm to learn a neural policy for solving Angry Birds levels. Our learning procedure is based on the Bootstrap algorithm, which was previously used to learn heuristic functions for solving classic heuristic search problems. BiB starts its learning procedure with a policy given by a randomly initialized neural network. This initial policy is used to guide the search algorithm on a set of procedurally generated Angry Birds levels. The levels the search algorithm is able to solve are used to improve the neural policy. We repeat this procedure a number of times, until solving all levels or reaching a time limit. We perform several experiments with different instances of our method and show that it can solve more levels than other approaches, including learning-based and rule-based methods.

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Section: Related Workmentioning

confidence: 99%

Birds in Boots: Learning to Play Angry Birds with Policy-Guided Search

Pereira

Chaimowicz

Lelis

2021

AIIDE

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“…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.…”

Section: E Future Ideasmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

The 2017 AIBIRDS Level Generation Competition

Stephenson

Renz

et al. 2019

IEEE Trans. Games

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This paper presents an overview of the sixth AIBIRDS competition, held at the 26th International Joint Conference on Artificial Intelligence. This competition tasked participants with developing an intelligent agent which can play the physics-based puzzle game Angry Birds. This game uses a sophisticated physics engine that requires agents to reason and predict the outcome of actions with only limited environmental information. Agents entered into this competition were required to solve a wide assortment of previously unseen levels within a set time limit. The physical reasoning and planning required to solve these levels are very similar to those of many realworld problems. This year's competition featured some of the best agents developed so far and even included several new AI techniques such as deep reinforcement learning. Within this paper we describe the framework, rules, submitted agents and results for this competition. We also provide some background information on related work and other video game AI competitions, as well as discussing some potential ideas for future AIBIRDS competitions and agent improvements.

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“…2013; Schiffer, Jourenko, and Lakemeyer 2016), logic programming (Calimeri et al 2016), heuristics (Dasgupta et al 2016), Bayesian inferences (Tziortziotis, Papagiannis, and Blekas 2016;Narayan-Chen, Xu, and Shavlik 2013), and structural analysis (Zhang and Renz 2014). However, none of these agents has ever come close to being the dominant performer across all levels (AIBirds 2017), indicating that these methods are best suited to specific situations.…”

Section: Introductionmentioning

confidence: 99%

Creating a Hyper-Agent for Solving Angry Birds Levels

Stephenson

Renz

2021

AIIDE

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Over the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video game Angry Birds. Many different agents and strategies have been developed to solve the complex and challenging physical reasoning problems associated with such a game. However, the performance of these various agents is non-transitive and varies significantly across different levels. No single agent dominates all situations presented, indicating that different procedures are better at solving certain levels than others. We therefore propose the construction of a hyper-agent that selects from a portfolio of sub-agents whichever it believes is best at solving any given level. This hyper-agent utilises key features that can be observed about a level to rank the available candidate algorithms based on their expected score.The proposed method exhibits a significant increase in performance over the individual sub-agents, and demonstrates the potential of using such an approach to solve other physics-based games or problems.

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Akbaba—An Agent for the Angry Birds AI Challenge Based on Search and Simulation

Cited by 6 publications

References 15 publications

Birds in Boots: Learning to Play Angry Birds with Policy-Guided Search

Birds in Boots: Learning to Play Angry Birds with Policy-Guided Search

The 2017 AIBIRDS Level Generation Competition

Creating a Hyper-Agent for Solving Angry Birds Levels

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