Title :
Learning parameters from manual task assignments for mobile robots
Author :
Seil An ; Dong Jun Kwak ; Kim, H.J.
Author_Institution :
Dept. of Mech. & Aerosp. Eng., Seoul Nat. Univ., Seoul, South Korea
Abstract :
For case where exist some threats in field of task assignment, threat avoidance is needed to be considered. Then both fast arrival and threat avoidance are used to form the performance measure. The policies of task assignment decide which factor is mostly considered between arrival time and threat avoidance. The purpose of this research is estimating the hidden policy from user´s manual task assignment. Using Naive-Bayes classification, we achieved satisfactory policy estimation for task assignment from testing several users.
Keywords :
Bayes methods; learning (artificial intelligence); mobile robots; path planning; pattern classification; Naive-Bayes classification; learning parameters; manual task assignments; mobile robots; performance measure; satisfactory policy estimation; task assignment policies; threat avoidance; Artificial neural networks; Manuals; Planning; Robots; Machine learning; Naive-Bayes classifier; Policy learning; Task assignment;
Conference_Titel :
Control, Automation and Systems (ICCAS), 2014 14th International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-8-9932-1506-9
DOI :
10.1109/ICCAS.2014.6987857