Learning Markov Decision Processes Based on Genetic Programming
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- @InProceedings{Wu:2022:ACIE,
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author = "Rong Wu and Jin Xu",
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booktitle = "2022 2nd Asia Conference on Information Engineering
(ACIE)",
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title = "Learning Markov Decision Processes Based on Genetic
Programming",
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year = "2022",
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pages = "72--76",
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abstract = "Model checking is used to verify the security of
communication protocols in which the behavior is
stochastic influenced by the environment. Automata
learning settles the problem of obtaining formal models
from observable data of black-box systems. It is
available for different variations of finite automata
to in model checking. Genetic Programming is a machine
learning technique that automatically generates
programs and outputs a fittest program. In this paper,
we present an approach to learn markov decision
progresses based on the framework of genetic
programming. The approach outputs the fittest model
with a set of system traces by refining iteratively
models. We evaluate our method on one probabilistic
system from the literature and 30 randomly generated
examples.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ACIE55485.2022.00023",
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month = jan,
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notes = "Also known as \cite{9831511}",
- }
Genetic Programming entries for
Rong Wu
Jin Xu
Citations