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Mech-Elites: Illuminating the Mechanic Space of GVG-AI

Authors:

Michael Cerny Green,

Julian TogeliusAuthors Info & Claims

FDG '20: Proceedings of the 15th International Conference on the Foundations of Digital Games

Article No.: 8, Pages 1 - 10

https://doi.org/10.1145/3402942.3402954

Published: 17 September 2020 Publication History

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Abstract

This paper introduces a fully automatic method of mechanic illumination for general video game level generation. Using the Constrained MAP-Elites algorithm and the GVG-AI framework, this system generates the simplest tile based levels that contain specific sets of game mechanics and also satisfy playability constraints. We apply this method to illuminate the mechanic space for four different games in GVG-AI: Zelda, Solarfox, Plants, and RealPortals. With this system, we can generate playable levels that contain different combinations of most of the possible mechanics. These levels can later be used to populate game tutorials that teach players how to use the mechanics of the game.

References

[1]

Alberto Alvarez, Steve Dahlskog, Jose Font, and Julian Togelius. 2019. Empowering quality diversity in dungeon design with interactive constrained MAP-Elites. In 2019 IEEE Conference on Games (CoG). IEEE, 1–8.

Digital Library

[2]

Daniel Ashlock. 2010. Automatic generation of game elements via evolution. In Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games. IEEE, 289–296.

[3]

Daniel Ashlock, Colin Lee, and Cameron McGuinness. 2011. Search-based procedural generation of maze-like levels. IEEE Transactions on Computational Intelligence and AI in Games 3, 3(2011), 260–273.

[4]

Philip Bontrager, Ahmed Khalifa, Andre Mendes, and Julian Togelius. 2016. Matching games and algorithms for general video game playing. In Twelfth Artificial Intelligence and Interactive Digital Entertainment Conference.

Digital Library

[5]

Michael Cook and Azalea Raad. 2019. Hyperstate space graphs for automated game analysis. In 2019 IEEE Conference on Games (CoG). IEEE, 1–8.

Digital Library

[6]

Marc Ebner, John Levine, Simon M Lucas, Tom Schaul, Tommy Thompson, and Julian Togelius. 2013. Towards a video game description language. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.

[7]

Raluca D Gaina, Diego Pérez-Liébana, and Simon M Lucas. 2016. General video game for 2 players: Framework and competition. In Computer Science and Electronic Engineering. IEEE, 186–191.

[8]

Daniele Gravina, Ahmed Khalifa, Antonios Liapis, Julian Togelius, and Georgios N Yannakakis. 2019. Procedural content generation through quality diversity. In 2019 IEEE Conference on Games (CoG). IEEE, 1–8.

Digital Library

[9]

Michael Cerny Green, Ahmed Khalifa, Gabriella AB Barros, Tiago Machado, Andy Nealen, and Julian Togelius. 2018. AtDELFI: automatically designing legible, full instructions for games. In Proceedings of the 13th International Conference on the Foundations of Digital Games. ACM, 17.

Digital Library

[10]

Michael Cerny Green, Ahmed Khalifa, Gabriella AB Barros, Tiago Machado, and Julian Togelius. 2019. Automatic Critical Mechanic Discovery in Video Games. arXiv preprint arXiv:1909.03094(2019).

[11]

Michael Cerny Green, Ahmed Khalifa, Gabriella AB Barros, and Julian Togellius. 2017. ” Press Space to Fire”: Automatic Video Game Tutorial Generation. In Thirteenth Artificial Intelligence and Interactive Digital Entertainment Conference.

[12]

Martin Jennings-Teats, Gillian Smith, and Noah Wardrip-Fruin. 2010. Polymorph: dynamic difficulty adjustment through level generation. In Proceedings of the 2010 Workshop on Procedural Content Generation in Games. 1–4.

Digital Library

[13]

Ahmed Khalifa, Michael Cerny Green, Gabriella Barros, and Julian Togelius. 2019. Intentional computational level design. In Proceedings of The Genetic and Evolutionary Computation Conference. 796–803.

Digital Library

[14]

Ahmed Khalifa, Michael Cerny Green, Diego Perez-Liebana, and Julian Togelius. 2017. General video game rule generation. In 2017 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 170–177.

Digital Library

[15]

Ahmed Khalifa, Scott Lee, Andy Nealen, and Julian Togelius. 2018. Talakat: Bullet hell generation through constrained map-elites. In Proceedings of The Genetic and Evolutionary Computation Conference. 1047–1054.

Digital Library

[16]

Ahmed Khalifa, Diego Perez-Liebana, Simon M Lucas, and Julian Togelius. 2016. General video game level generation. In Genetic and Evolutionary Computation Conference. ACM, 253–259.

Digital Library

[17]

Steven Orla Kimbrough, Gary J Koehler, Ming Lu, and David Harlan Wood. 2008. On a feasible–infeasible two-population (fi-2pop) genetic algorithm for constrained optimization: Distance tracing and no free lunch. European Journal of Operational Research 190, 2 (2008), 310–327.

[18]

Jean-Baptiste Mouret and Jeff Clune. 2015. Illuminating search spaces by mapping elites. arXiv preprint arXiv:1504.04909(2015).

[19]

Diego Perez-Liebana, Jialin Liu, Ahmed Khalifa, Raluca D Gaina, Julian Togelius, and Simon M Lucas. 2019. General video game ai: a multi-track framework for evaluating agents, games and content generation algorithms. Transactions on Games(2019).

[20]

Diego Perez-Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, and Simon M Lucas. 2016. General video game ai: Competition, challenges and opportunities. In AAAI Conference on Artificial Intelligence.

[21]

Miguel Sicart. 2008. Defining game mechanics. Game Studies 8, 2 (2008), n.

[22]

Adam M Smith, Erik Andersen, Michael Mateas, and Zoran Popović. 2012. A case study of expressively constrainable level design automation tools for a puzzle game. In International Conference on the Foundations of Digital Games. ACM, 156–163.

Digital Library

[23]

Julian Togelius, Sergey Karakovskiy, and Robin Baumgarten. 2010. The 2009 mario ai competition. In IEEE Congress on Evolutionary Computation. IEEE, 1–8.

[24]

Julian Togelius, Georgios N Yannakakis, Kenneth O Stanley, and Cameron Browne. 2011. Search-based procedural content generation: A taxonomy and survey. IEEE Transactions on Computational Intelligence and AI in Games 3, 3(2011), 172–186.

[25]

Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius, Jialin Liu, and Diego Perez-Liebana. 2018. Deep Reinforcement Learning for General Video Game AI. In Computational Intelligence and Games. IEEE, 1–8.

[26]

Vivek R Warriar, Carmen Ugarte, John R Woodward, and Laurissa Tokarchuk. 2019. PlayMapper: Illuminating Design Spaces of Platform Games. In 2019 IEEE Conference on Games (CoG). IEEE, 1–4.

Cited By

Charity MDave IKhalifa ATogelius J(2024)Baba is Y'all 2.0: Design and Investigation of a Collaborative Mixed-Initiative SystemIEEE Transactions on Games10.1109/TG.2022.322352716:1(75-89)Online publication date: Mar-2024
https://doi.org/10.1109/TG.2022.3223527
Pereira LViana BToledo C(2024)A System for Orchestrating Multiple Procedurally Generated Content for Different Player ProfilesIEEE Transactions on Games10.1109/TG.2022.321378116:1(64-74)Online publication date: Mar-2024
https://doi.org/10.1109/TG.2022.3213781
Alvarez AFont J(2022)TropeTwist: Trope-based Narrative Structure GenerationProceedings of the 17th International Conference on the Foundations of Digital Games10.1145/3555858.3563271(1-8)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1145/3555858.3563271
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Mech-Elites: Illuminating the Mechanic Space of GVG-AI

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Published In

cover image ACM Other conferences

FDG '20: Proceedings of the 15th International Conference on the Foundations of Digital Games

September 2020

804 pages

ISBN:9781450388078

DOI:10.1145/3402942

Editors:
Georgios N. Yannakakis
University of Malta
,
Antonios Liapis
University of Malta
,
Penny Kyburz
Australian National University
,
Vanessa Volz
Modl.AI
,
Foaad Khosmood
California Polytechnic State University
,
Phil Lopes
École Polytechnique Fédèrale de Lausanne

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 September 2020

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National Science Foundation

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FDG '20

FDG '20: International Conference on the Foundations of Digital Games

September 15 - 18, 2020

Bugibba, Malta

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Overall Acceptance Rate 152 of 415 submissions, 37%

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Citations

Cited By

Charity MDave IKhalifa ATogelius J(2024)Baba is Y'all 2.0: Design and Investigation of a Collaborative Mixed-Initiative SystemIEEE Transactions on Games10.1109/TG.2022.322352716:1(75-89)Online publication date: Mar-2024
https://doi.org/10.1109/TG.2022.3223527
Pereira LViana BToledo C(2024)A System for Orchestrating Multiple Procedurally Generated Content for Different Player ProfilesIEEE Transactions on Games10.1109/TG.2022.321378116:1(64-74)Online publication date: Mar-2024
https://doi.org/10.1109/TG.2022.3213781
Alvarez AFont J(2022)TropeTwist: Trope-based Narrative Structure GenerationProceedings of the 17th International Conference on the Foundations of Digital Games10.1145/3555858.3563271(1-8)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1145/3555858.3563271
Alvarez AFont JTogelius J(2022)Story Designer: Towards a Mixed-Initiative Tool to Create Narrative StructuresProceedings of the 17th International Conference on the Foundations of Digital Games10.1145/3555858.3555929(1-9)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1145/3555858.3555929
Withington OTokarchuk L(2022)Compressing and Comparing the Generative Spaces of Procedural Content Generators2022 IEEE Conference on Games (CoG)10.1109/CoG51982.2022.9893615(143-150)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1109/CoG51982.2022.9893615
Green MKhalifa ACharity MBhaumik DTogelius J(2022)Predicting Personas Using Mechanic Frequencies and Game State Traces2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870406(1-8)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1109/CEC55065.2022.9870406
Gaier AStoddart JVillaggi LBentley P(2022)T-DominOParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14721-0_19(263-277)Online publication date: 15-Aug-2022
https://doi.org/10.1007/978-3-031-14721-0_19
Sarkar ACooper S(2021)Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational AutoencoderThe 16th International Conference on the Foundations of Digital Games (FDG) 202110.1145/3472538.3472545(1-11)Online publication date: 21-Oct-2021
https://doi.org/10.1145/3472538.3472545
Charity MKhalifa ATogelius J(2020)Baba is Y’all: Collaborative Mixed-Initiative Level Design2020 IEEE Conference on Games (CoG)10.1109/CoG47356.2020.9231807(542-549)Online publication date: Aug-2020
https://doi.org/10.1109/CoG47356.2020.9231807
Omidshafiei STuyls KCzarnecki WSantos FRowland MConnor JHennes DMuller PPérolat JVylder BGruslys AMunos R(2020)Navigating the landscape of multiplayer gamesNature Communications10.1038/s41467-020-19244-411:1Online publication date: 5-Nov-2020
https://doi.org/10.1038/s41467-020-19244-4

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