Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2024 IEEE Conference on Games, CoG 2024 |
| Publisher | IEEE Computer Society |
| ISBN (electronic) | 9798350350678 |
| ISBN (print) | 979-8-3503-5068-5 |
| Publication status | Published - 5 Aug 2024 |
| Event | 6th Annual IEEE Conference on Games, CoG 2024 - Milan, Italy Duration: 5 Aug 2024 → 8 Aug 2024 |
Publication series
| Name | IEEE Conference on Computatonal Intelligence and Games, CIG |
|---|---|
| ISSN (Print) | 2325-4270 |
| ISSN (electronic) | 2325-4289 |
Abstract
Markov Junior is a probabilistic programming language used for procedural content generation across various domains. However, its reliance on manually crafted and tuned probabilistic rule sets, also called grammars, presents a significant bottleneck, diverging from approaches that allow rule learning from examples. In this paper, we propose a novel solution to this challenge by introducing a genetic programming-based optimization framework for learning hierarchical rule sets automatically. Our proposed method 'Markov Senior' focuses on extracting positional and distance relations from single input samples to construct probabilistic rules to be used by Markov Junior. Using a Kullback-Leibler divergence-based fitness measure, we search for grammars to generate content that is coherent with the given sample. To enhance scalability, we introduce a divide-and-conquer strategy that enables the efficient generation of large-scale content We validate our approach through experiments in generating image-based content and Super Mario levels, demonstrating its flexibility and effectiveness. In this way, 'Markov Senior' allows for the wider application of Markov Junior for tasks in which an example may be available, but the design of a generative rule set is infeasible.
Keywords
- Genetic Programming, Markov Junior, Procedural Content Generation, Super Mario Level Generation
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
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Proceedings of the 2024 IEEE Conference on Games, CoG 2024. IEEE Computer Society, 2024. (IEEE Conference on Computatonal Intelligence and Games, CIG).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Markov Senior
T2 - 6th Annual IEEE Conference on Games, CoG 2024
AU - Oguz, Mehmet Kayra
AU - Dockhorn, Alexander
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024/8/5
Y1 - 2024/8/5
N2 - Markov Junior is a probabilistic programming language used for procedural content generation across various domains. However, its reliance on manually crafted and tuned probabilistic rule sets, also called grammars, presents a significant bottleneck, diverging from approaches that allow rule learning from examples. In this paper, we propose a novel solution to this challenge by introducing a genetic programming-based optimization framework for learning hierarchical rule sets automatically. Our proposed method 'Markov Senior' focuses on extracting positional and distance relations from single input samples to construct probabilistic rules to be used by Markov Junior. Using a Kullback-Leibler divergence-based fitness measure, we search for grammars to generate content that is coherent with the given sample. To enhance scalability, we introduce a divide-and-conquer strategy that enables the efficient generation of large-scale content We validate our approach through experiments in generating image-based content and Super Mario levels, demonstrating its flexibility and effectiveness. In this way, 'Markov Senior' allows for the wider application of Markov Junior for tasks in which an example may be available, but the design of a generative rule set is infeasible.
AB - Markov Junior is a probabilistic programming language used for procedural content generation across various domains. However, its reliance on manually crafted and tuned probabilistic rule sets, also called grammars, presents a significant bottleneck, diverging from approaches that allow rule learning from examples. In this paper, we propose a novel solution to this challenge by introducing a genetic programming-based optimization framework for learning hierarchical rule sets automatically. Our proposed method 'Markov Senior' focuses on extracting positional and distance relations from single input samples to construct probabilistic rules to be used by Markov Junior. Using a Kullback-Leibler divergence-based fitness measure, we search for grammars to generate content that is coherent with the given sample. To enhance scalability, we introduce a divide-and-conquer strategy that enables the efficient generation of large-scale content We validate our approach through experiments in generating image-based content and Super Mario levels, demonstrating its flexibility and effectiveness. In this way, 'Markov Senior' allows for the wider application of Markov Junior for tasks in which an example may be available, but the design of a generative rule set is infeasible.
KW - Genetic Programming
KW - Markov Junior
KW - Procedural Content Generation
KW - Super Mario Level Generation
UR - http://www.scopus.com/inward/record.url?scp=85203535988&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2408.05959
DO - 10.48550/arXiv.2408.05959
M3 - Conference contribution
AN - SCOPUS:85203535988
SN - 979-8-3503-5068-5
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - Proceedings of the 2024 IEEE Conference on Games, CoG 2024
PB - IEEE Computer Society
Y2 - 5 August 2024 through 8 August 2024
ER -