Markov Senior - Learning Markov Junior Grammars to Generate User-specified Content
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @InProceedings{Oguz:2024:CoG,
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author = "Mehmet Kayra Oguz and Alexander Dockhorn",
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title = "Markov Senior - Learning Markov Junior Grammars to
Generate User-specified Content",
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booktitle = "2024 IEEE Conference on Games (CoG)",
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year = "2024",
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month = aug,
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keywords = "genetic algorithms, genetic programming, Computer
languages, Procedural generation, Scalability,
Probabilistic logic, Genetics, Grammar, Task analysis,
Markov Junior, Procedural Content Generation, Super
Mario Level Generation",
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ISSN = "2325-4289",
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URL = "
https://www.tnt.uni-hannover.de/papers/data/1736/_2024__COG__Markov_Senior___Learning_Markov_Junior_Grammars_to_Generate_User_specified_Content.pdf",
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DOI = "
doi:10.1109/CoG60054.2024.10645650",
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size = "8 pages",
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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
optimisation 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.",
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notes = "Also known as \cite{10645650}
\cite{OguDoc2024}
Faculty of EECS, Leibniz University Hannover, Hannover,
Germany",
- }
Genetic Programming entries for
Mehmet Kayra Oguz
Alexander Dockhorn
Citations