Authors:
Raafat Mahmoud
;
Atsushi Ueno
and
Shoji Tatsumi
Affiliation:
Osaka City University, Japan
Keyword(s):
Humanoid robot, Game strategy, Learning from observation, Structured interview, Long-term memory, Sensory memory, Working memory.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Cognitive Systems
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge Representation and Reasoning
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
We propose a new approach for teaching a humanoid-robot a task online without pre-set data provided in advance. In our approach, human acts as a collaborator and also as a teacher. The proposed approach enables the humanoid-robot to learn a task through multi-component interactive architecture. The components are designed with the respect to human methodology for learning a task through empirical interactions. For efficient performance, the components are isolated within one single API. Our approach can be divided into five main roles: perception, representation, state/knowledge-up-dating, decision making and expression. A conducted empirical experiment for the proposed approach is to be done by teaching a Fujitsu’s humanoid-robot "Hoap-3" an X-O game strategy and its results are to be done and explained. Important component such as observation, structured interview, knowledge integration and decision making are described for teaching the robot the game strategy while conducting the
experiment.
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