IORand: A Procedural Videogame Level Generator Based on a Hybrid PCG Algorithm
In this work we present the intelligent orchestrator of random generators (IORand), a hybrid procedural content generation (PCG) algorithm, driven by game experience, based on reinforcement learning and semi-random content generation methods. Our study includes a presentation of current PCG techniqu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/8/3792 |
| _version_ | 1797437028389879808 |
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| author | Marco A. Moreno-Armendáriz Hiram Calvo José A. Torres-León Carlos A. Duchanoy |
| author_facet | Marco A. Moreno-Armendáriz Hiram Calvo José A. Torres-León Carlos A. Duchanoy |
| author_sort | Marco A. Moreno-Armendáriz |
| collection | DOAJ |
| description | In this work we present the intelligent orchestrator of random generators (IORand), a hybrid procedural content generation (PCG) algorithm, driven by game experience, based on reinforcement learning and semi-random content generation methods. Our study includes a presentation of current PCG techniques and why a hybridization of approaches has become a new trend with promising results in the area. Moreover, the design of a new method for evaluating video game levels is presented, aimed at evaluating game experiences, based on graphs, which allows identifying the type of interaction that the player will have with the level. Then, the design of our hybrid PCG algorithm, IORand, whose reward function is based on the proposed level evaluation method, is presented. Finally, a study was conducted on the performance of our algorithm to generate levels of three different game experiences, from which we demonstrate the ability of IORand to satisfactorily and consistently solve the generation of levels that provide specific game experiences. |
| first_indexed | 2024-03-09T11:13:00Z |
| format | Article |
| id | doaj.art-767e901b621e41448af6c74e2e56f3ff |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T11:13:00Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-767e901b621e41448af6c74e2e56f3ff2023-12-01T00:38:50ZengMDPI AGApplied Sciences2076-34172022-04-01128379210.3390/app12083792IORand: A Procedural Videogame Level Generator Based on a Hybrid PCG AlgorithmMarco A. Moreno-Armendáriz0Hiram Calvo1José A. Torres-León2Carlos A. Duchanoy3Computational Cognitive Sciences Laboratory, Center for Computing Research, Instituto Politécnico Nacional, Mexico City 07738, MexicoComputational Cognitive Sciences Laboratory, Center for Computing Research, Instituto Politécnico Nacional, Mexico City 07738, MexicoComputational Cognitive Sciences Laboratory, Center for Computing Research, Instituto Politécnico Nacional, Mexico City 07738, MexicoGus Chat, Av. Paseo de la Reforma 26-Piso 19, Mexico City 06600, MexicoIn this work we present the intelligent orchestrator of random generators (IORand), a hybrid procedural content generation (PCG) algorithm, driven by game experience, based on reinforcement learning and semi-random content generation methods. Our study includes a presentation of current PCG techniques and why a hybridization of approaches has become a new trend with promising results in the area. Moreover, the design of a new method for evaluating video game levels is presented, aimed at evaluating game experiences, based on graphs, which allows identifying the type of interaction that the player will have with the level. Then, the design of our hybrid PCG algorithm, IORand, whose reward function is based on the proposed level evaluation method, is presented. Finally, a study was conducted on the performance of our algorithm to generate levels of three different game experiences, from which we demonstrate the ability of IORand to satisfactorily and consistently solve the generation of levels that provide specific game experiences.https://www.mdpi.com/2076-3417/12/8/3792procedural content generationartificial intelligencereinforcement learningsemi-random generationhybrid algorithms |
| spellingShingle | Marco A. Moreno-Armendáriz Hiram Calvo José A. Torres-León Carlos A. Duchanoy IORand: A Procedural Videogame Level Generator Based on a Hybrid PCG Algorithm Applied Sciences procedural content generation artificial intelligence reinforcement learning semi-random generation hybrid algorithms |
| title | IORand: A Procedural Videogame Level Generator Based on a Hybrid PCG Algorithm |
| title_full | IORand: A Procedural Videogame Level Generator Based on a Hybrid PCG Algorithm |
| title_fullStr | IORand: A Procedural Videogame Level Generator Based on a Hybrid PCG Algorithm |
| title_full_unstemmed | IORand: A Procedural Videogame Level Generator Based on a Hybrid PCG Algorithm |
| title_short | IORand: A Procedural Videogame Level Generator Based on a Hybrid PCG Algorithm |
| title_sort | iorand a procedural videogame level generator based on a hybrid pcg algorithm |
| topic | procedural content generation artificial intelligence reinforcement learning semi-random generation hybrid algorithms |
| url | https://www.mdpi.com/2076-3417/12/8/3792 |
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