Behavior control of multiple agents by Cartesian Genetic Programming equipped with sharing sub-programs among agents

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Hara:2015:ieeeIWCIA,

• author = "Akira Hara and Jun-ichi Kushida and Tomoya Okita and Tetsuyuki Takahama",
• booktitle = "8th IEEE International Workshop on Computational Intelligence and Applications (IWCIA)",
• title = "Behavior control of multiple agents by Cartesian Genetic Programming equipped with sharing sub-programs among agents",
• year = "2015",
• pages = "71--76",
• address = "Hiroshima, Japan",
• keywords = "genetic algorithms, genetic programming, Artificial neural networks, Cloning, Mathematical model, Multi-agent systems, Optimisation, Cartesian Genetic Programming, Evolutionary Computation, Multi-Agent Systems",
• URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7449465",
• DOI = "doi:10.1109/IWCIA.2015.7449465",
• ISSN = "1883-3977",
• month = "6-7 " # nov,
• abstract = "In this paper, we focus on evolutionary optimisation of multi-agent behaviour. There are two representative models for multi-agent control, homogeneous and heterogeneous models. In the homogeneous model, all agents are controlled by the same controller. Therefore, it is difficult to realize complex cooperative behaviour such as division of labours. In contrast, in the heterogeneous model, respective agents can play different roles for cooperative tasks. However, the search space becomes too large to optimise respective controllers. To solve the problems, we propose a new multi-agent control model based on Cartesian Genetic Programming (CGP). In CGP, each individual represents a graph-structural program and it can have multiple outputs. The feature is used for controlling multiple agents in our model. In addition, we propose a new genetic operator dedicated to multi-agent control. Our method enables multiple agents to not only take different actions according to their own roles but also share sub-programs if the same behaviour is needed for solving problems. We applied our method to a food foraging problem. The experimental results showed that the performance of our method is superior to those of the conventional models.",
• notes = "Also known as \cite{7449465}",

}

Genetic Programming entries for Akira Hara Jun-ichi Kushida Tomoya Okita Tetsuyuki Takahama

Citations