Knowledge acquisition of fuzzy control rules for mobile robots using DNA coding method and pseudo-bacterial GA

1997 ◽  
pp. 126-135 ◽  
Author(s):  
Tomohiro Yoshikawa ◽  
Takeshi Furuhashi ◽  
Yoshiki Uchikawa
Keyword(s):  
Fuzzy Control ◽  
Mobile Robots ◽  
Knowledge Acquisition ◽  
Dna Coding ◽  
Control Rules ◽  
Coding Method

scholarly journals Reinforcement Leaning of Fuzzy Control Rules with Context-Specitic Segmentation of Actions

2002 ◽  
Vol 6 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Hideki Yamagishi ◽  
◽  
Hiroshi Kawakami ◽  
Tadashi Horiuchi ◽  
Osamu Katai ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Decision Tree ◽  
Fuzzy Control ◽  
Knowledge Acquisition ◽  
Fuzzy Inference ◽  
Experimental Information ◽  
Proper Action ◽  
Proper Actions ◽  
Control Rules ◽  
Action Decision

Knowledge acquisition mainly involves two approaches: deriving general or abstract rules from human expertise such as heuristics of target systems, refined properly using further information, and extracting proper rules from experimental information, i.e., information on rewards and penalties obtained from all the possible alternative rules initially prepared ‐ our approach. Reinforcement learning methods are applied to problems where meaningful I/O sets cannot be specified beforehand. There are, however few algorithms to extract heuristics for action selection by using results of reinforcement learning. We propose a way to apply symbolic processing methods such as C4.5 to results of reinforcement learning where methods of fuzzy inference are incorporated. We also derive a proper action decision tree where conditions of proper actions for agents are effectively integrated and simplified.

Adaptive Neural-Fuzzy Control of Nonholonomic Mobile Robots

2006 ◽  
pp. 229-265 ◽  
Author(s):  
Tong Heng Lee ◽  
Frank Lewis ◽  
Shuzhi Sam Ge ◽  
Fan Hong
Keyword(s):  
Fuzzy Control ◽  
Mobile Robots ◽  
Nonholonomic Mobile Robots ◽  
Neural Fuzzy
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