Genetic Programming for Optimizing Behavioral Rules of Agents Mimicking Human Behavior Patterns

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Kakizako:2022:SCIS,

• author = "Kosuke Kakizako and Yoshiko Hanada",
• title = "Genetic Programming for Optimizing Behavioral Rules of Agents Mimicking Human Behavior Patterns",
• booktitle = "2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS+ISIS)",
• year = "2022",
• month = nov,
• keywords = "genetic algorithms, genetic programming, Training, Energy consumption, Linear programming, Search problems, Behavioural sciences, Complexity theory, behaviour rule, agent control",
• DOI = "doi:10.1109/SCISISIS55246.2022.10002152",
• 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.",
• notes = "Also known as \cite{10002152}",

}

Genetic Programming entries for Kosuke Kakizako Yoshiko Hanada

Citations