DocumentCode :
1797503
Title :
Robot team learning enhancement using Human Advice
Author :
Girard, Justin ; Emami, M. Reza
Author_Institution :
Inst. for Aerosp. Studies, Univ. of Toronto, Toronto, ON, Canada
fYear :
2014
fDate :
9-12 Dec. 2014
Firstpage :
1
Lastpage :
7
Abstract :
The paper discusses the augmentation of the Concurrent Individual and Social Learning (CISL) mechanism with a new Human Advice Layer (HAL). The new layer is characterized by a Gaussian Mixture Model (GMM), which is trained on human experience data. The CISL mechanism consists of the Individual Performance and Task Allocation Markov Decision Processes (MDP), and the HAL can provide preferred action selection policies to the individual agents. The data utilized for training the GMM is collected using a heterogeneous team foraging simulation. When leveraging human experience in the multi-agent learning process, the team performance is enhanced significantly.
Keywords :
Gaussian processes; Markov processes; human-robot interaction; learning (artificial intelligence); mixture models; multi-agent systems; multi-robot systems; CISL mechanism; GMM; Gaussian mixture model; HAL; MDP; Markov decision process; concurrent individual and social learning; human advice layer; multi-agent learning process; robot team learning enhancement; task allocation; Games; Gaussian mixture model; Indexes; Resource management; Robot kinematics; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotic Intelligence In Informationally Structured Space (RiiSS), 2014 IEEE Symposium on
Conference_Location :
Orlando, FL
Type :
conf
DOI :
10.1109/RIISS.2014.7009184
Filename :
7009184
Link To Document :
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