Self-adaptation for Mobile Robot Algorithms Using Organic Computing Principles

Author(s):  
Jan Hartmann ◽  
Walter Stechele ◽  
Erik Maehle
2006 ◽  
Vol 12 (3) ◽  
pp. 353-380 ◽  
Author(s):  
Jacob Hurst ◽  
Larry Bull

For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.


2019 ◽  
Vol 139 (9) ◽  
pp. 1041-1050
Author(s):  
Hiroyuki Nakagomi ◽  
Yoshihiro Fuse ◽  
Hidehiko Hosaka ◽  
Hironaga Miyamoto ◽  
Takashi Nakamura ◽  
...  

2015 ◽  
Vol 135 (8) ◽  
pp. 944-953 ◽  
Author(s):  
Kazuki Tanaka ◽  
Hiroyuki Ukida

2013 ◽  
Vol 133 (5) ◽  
pp. 502-509 ◽  
Author(s):  
Kouhei Komiya ◽  
Shunsuke Miyashita ◽  
Yutaka Maruoka ◽  
Yutaka Uchimura

2020 ◽  
Vol 13 (1) ◽  
pp. 27
Author(s):  
Shaaban Ali Salman ◽  
Qais A. Khasawneh ◽  
Mohammad A. Jaradat ◽  
Mansour Y. Alramlawi

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