Paper Info
Title
A Novel Hierarchical Constructive BackPropagation with Memory for Teaching a Robot the Names of Things
Abstract
In recent years, there has been a growing attention to develop a Human-like Robot controller that hopes to move the robots closer to face real world applications. Several approaches have been proposed to support the learning phase in such a controller, such as learning through observation and\or a direct guidance from the user. These approaches, however, require incremental learning and memorizing techniques, where the robot can design its internal system and keep retraining it overtime. This study, therefore, investigates a new idea to develop incremental learning and memory model, we called, a Hierarchical Constructive BackPropagation with Memory (HCBPM). The validity of the model was tested in teaching a robot a group of names (colors). The experimental results indicate the efficiency of the model to build a social learning environment between the user and the robot. The robot could learn various color names and its different phases, and retrieve these data easily to teach another user what it had learned.
Year
DOI
Venue
2009
10.1007/978-3-642-10677-4_51
ICONIP (1)
Keywords
Field
DocType
internal system,incremental learning and memory,memory model,memorizing technique,direct guidance,novel hierarchical constructive backpropagation,social learning environment,human-like robot controller,hierarchical constructive backpropagation,different phase,incremental learning,constructive backpropagation.,backpropagation,social learning
Robot learning,Control theory,Computer science,Constructive,Memory model,Social learning,Artificial intelligence,Backpropagation,Robot,Memorization,Machine learning
Conference
Volume
ISSN
Citations 
5863
0302-9743
0
PageRank 
References 
Authors
0.34
10
3
Name
Order
Citations
PageRank
Fady Alnajjar16612.23
Abdul Rahman Hafiz2224.08
Kazuyuki Murase3103875.66