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FALCONET: FORCE-FEEDBACK APPROACH FOR LEARNING FROM COACHING AND OBSERVATION USING NATURAL AND EXPERIENTIAL TRAINING

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Date Issued:
2009
Abstract/Description:
Building an intelligent agent model from scratch is a difficult task. Thus, it would be preferable to have an automated process perform this task. There have been many manual and automatic techniques, however, each of these has various issues with obtaining, organizing, or making use of the data. Additionally, it can be difficult to get perfect data or, once the data is obtained, impractical to get a human subject to explain why some action was performed. Because of these problems, machine learning from observation emerged to produce agent models based on observational data. Learning from observation uses unobtrusive and purely observable information to construct an agent that behaves similarly to the observed human. Typically, an observational system builds an agent only based on prerecorded observations. This type of system works well with respect to agent creation, but lacks the ability to be trained and updated on-line. To overcome these deficiencies, the proposed system works by adding an augmented force-feedback system of training that senses the agents intentions haptically. Furthermore, because not all possible situations can be observed or directly trained, a third stage of learning from practice is added for the agent to gain additional knowledge for a particular mission. These stages of learning mimic the natural way a human might learn a task by first watching the task being performed, then being coached to improve, and finally practicing to self improve. The hypothesis is that a system that is initially trained using human recorded data (Observational), then tuned and adjusted using force-feedback (Instructional), and then allowed to perform the task in different situations (Experiential) will be better than any individual step or combination of steps.
Title: FALCONET: FORCE-FEEDBACK APPROACH FOR LEARNING FROM COACHING AND OBSERVATION USING NATURAL AND EXPERIENTIAL TRAINING.
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Name(s): Stein, Gary, Author
Gonzalez, Avelino, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2009
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Building an intelligent agent model from scratch is a difficult task. Thus, it would be preferable to have an automated process perform this task. There have been many manual and automatic techniques, however, each of these has various issues with obtaining, organizing, or making use of the data. Additionally, it can be difficult to get perfect data or, once the data is obtained, impractical to get a human subject to explain why some action was performed. Because of these problems, machine learning from observation emerged to produce agent models based on observational data. Learning from observation uses unobtrusive and purely observable information to construct an agent that behaves similarly to the observed human. Typically, an observational system builds an agent only based on prerecorded observations. This type of system works well with respect to agent creation, but lacks the ability to be trained and updated on-line. To overcome these deficiencies, the proposed system works by adding an augmented force-feedback system of training that senses the agents intentions haptically. Furthermore, because not all possible situations can be observed or directly trained, a third stage of learning from practice is added for the agent to gain additional knowledge for a particular mission. These stages of learning mimic the natural way a human might learn a task by first watching the task being performed, then being coached to improve, and finally practicing to self improve. The hypothesis is that a system that is initially trained using human recorded data (Observational), then tuned and adjusted using force-feedback (Instructional), and then allowed to perform the task in different situations (Experiential) will be better than any individual step or combination of steps.
Identifier: CFE0002746 (IID), ucf:48157 (fedora)
Note(s): 2009-08-01
Ph.D.
Engineering and Computer Science, School of Electrical Engineering and Computer Science
Doctorate
This record was generated from author submitted information.
Subject(s): NEAT
Neural Networks
Genetic Algorithms
Machine Learning
Observational Learning
Coaching
PSO
Genetic Programming
Instructional Learning
Experiential Learning
Haptics
PIGEON
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0002746
Restrictions on Access: public
Host Institution: UCF

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