The 2017 AIBIRDS Level Generation Competition

Stephenson, Matthew; Renz, Jochen; Ge, Xiaoyu; Ferreira, Lucas Lima; Togelius, Julian; Zhang, Peng

doi:10.1109/tg.2018.2854896

Cited by 20 publications

(19 citation statements)

References 47 publications

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“…Our game domain is a clone of Angry Birds, called Science Birds [5] for the visualization and simulation of levels. It is a very active game domain in the field of procedural level generation, where level generation competitions are held every year [23].…”

Section: Angry Birdsmentioning

confidence: 99%

Level generation for angry birds with sequential VAE and latent variable evolution

Tanabe

Fukuchi

Sakuma

et al. 2021

Proceedings of the Genetic and Evolutionary Computation Conference

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Video game level generation based on machine learning (ML), in particular, deep generative models, has attracted attention as a technique to automate level generation. However, applications of existing ML-based level generations are mostly limited to tile-based level representation. When ML techniques are applied to game domains with non-tile-based level representation, such as Angry Birds, where objects in a level are specified by real-valued parameters, ML often fails to generate playable levels. In this study, we develop a deep-generative-model-based level generation for the game domain of Angry Birds. To overcome these drawbacks, we propose a sequential encoding of a level and process it as text data, whereas existing approaches employ a tile-based encoding and process it as an image. Experiments show that the proposed level generator drastically improves the stability and diversity of generated levels compared with existing approaches. We apply latent variable evolution with the proposed generator to control the feature of a generated level computed through an AI agent's play, while keeping the level stable and natural. CCS CONCEPTS• Computing methodologies → Neural networks; Learning latent representations;

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Section: Angry Birdsmentioning

confidence: 99%

Level generation for angry birds with sequential VAE and latent variable evolution

Tanabe

Fukuchi

Sakuma

et al. 2021

Proceedings of the Genetic and Evolutionary Computation Conference

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“…• Pig Shooter: The strategy of the Pig Shooter is to directly shoot at the pigs. The agent shoots the bird on the slingshot by randomly selecting a pig and a trajectory to shoot the pig [62].…”

Section: Baseline Agentsmentioning

confidence: 99%

Phy-Q: A Testbed for Physical Reasoning

Cheng¹,

Pinto²,

Gamage³

et al. 2021

Preprint

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Humans are well-versed in reasoning about the behaviors of physical objects when choosing actions to accomplish tasks, while it remains a major challenge for AI. To facilitate research addressing this problem, we propose a new benchmark that requires an agent to reason about physical scenarios and take an action accordingly. Inspired by the physical knowledge acquired in infancy and the capabilities required for robots to operate in real-world environments, we identify 15 essential physical scenarios. For each scenario, we create a wide variety of distinct task templates, and we ensure all the task templates within the same scenario can be solved by using one specific physical rule. By having such a design, we evaluate two distinct levels of generalization, namely the local generalization and the broad generalization. We conduct an extensive evaluation with human players, learning agents with varying input types and architectures, and heuristic agents with different strategies. The benchmark gives a Phy-Q (physical reasoning quotient) score that reflects the physical reasoning ability of the agents. Our evaluation shows that 1) all agents fail to reach human performance, and 2) learning agents, even with good local generalization ability, struggle to learn the underlying physical reasoning rules and fail to generalize broadly. We encourage the development of intelligent agents with broad generalization abilities in physical domains. URL: https://github.com/phy-q/benchmark * Equal contribution Preprint. Under review.

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“…Unlike for the Mario AI level generation track, the generators here are not supposed to generate levels for a particular game; instead, they have to generate levels for any game that is given to them (as specified in a particular game description language). Another game-based AI competition with a level generation track is the Angry Birds competition, where the level generation track challenges competitors to submit generators that can create interesting Angry Birds levels with an appropriate level of difficulty [33].…”

Section: Related Workmentioning

confidence: 99%

“…As far as we are aware, all existing open-ended AI research is concerned with the behavior of agents in an environment; this competition is an attempt to bring the open-ended mindset to creating generators of environments. The GDMC competition also differs from existing PCG work and competitions [14,13,26] in that it focuses on holistic and adaptive content generation. Holistic PCG means that we are not looking at the generation of one aspect of the environment on its own, but rather at all of them together: buildings, paths, natural features, backstories, and so on.…”

Section: Introductionmentioning

confidence: 99%

The AI Settlement Generation Challenge in Minecraft

et al. 2020

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This article outlines what we learned from the first year of the AI Settlement Generation Competition in Minecraft, a competition about producing AI programs that can generate interesting settlements in Minecraft for an unseen map. This challenge seeks to focus research into adaptive and holistic procedural content generation. Generating Minecraft towns and villages given existing maps is a suitable task for this, as it requires the generated content to be adaptive, functional, evocative and aesthetic at the same time. Here, we present the results from the first iteration of the competition. We discuss the evaluation methodology, present the different technical approaches by the competitors, and outline the open problems.

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The 2017 AIBIRDS Level Generation Competition

Cited by 20 publications

References 47 publications

Level generation for angry birds with sequential VAE and latent variable evolution

Level generation for angry birds with sequential VAE and latent variable evolution

Phy-Q: A Testbed for Physical Reasoning

The AI Settlement Generation Challenge in Minecraft

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