Genetic Programming for Optimizing Behavioral Rules of Agents Mimicking Human Behavior Patterns
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Kakizako:2022:SCIS,
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author = "Kosuke Kakizako and Yoshiko Hanada",
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title = "Genetic Programming for Optimizing Behavioral Rules of
Agents Mimicking Human Behavior Patterns",
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booktitle = "2022 Joint 12th International Conference on Soft
Computing and Intelligent Systems and 23rd
International Symposium on Advanced Intelligent Systems
(SCIS+ISIS)",
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year = "2022",
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month = nov,
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keywords = "genetic algorithms, genetic programming, Training,
Energy consumption, Linear programming, Search
problems, Behavioural sciences, Complexity theory,
behaviour rule, agent control",
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DOI = "doi:10.1109/SCISISIS55246.2022.10002152",
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abstract = "Genetic Programming (GP) is one of the effective
methods to automatically generate a structure of
behavior rule of agent such as robots. In optimization
of a behavior rule of an agent to achieve a task, it is
important to generate robust rules that work well in an
environment involving slight errors. This paper shows a
new approach for generating a flexible behavior rule of
agent achieving task in an inaccurate environment. In
our approach, we focus on the flexibility of humans'
behavior to apply learned knowledge to similar
patterns. Here we extend the Santa Fe Trail problem
which is one of artificial ant problems, and introduce
a degree of imitation of human operations to the
objective function. Through the numerical experiments,
we show that GP with a new objective function can
generate rules that work well in an environment with
errors.",
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notes = "Also known as \cite{10002152}",
- }
Genetic Programming entries for
Kosuke Kakizako
Yoshiko Hanada
Citations