Online knowledge acquisition and general problem solving in a real world by humanoid robots

Authors:
Naoya Makibuchi;Furao Shen;Osamu Hasegawa
Affiliations:
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan;The State Key Laboratory for Novel Software Technology, Nanjing University, China;Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Year:
2010

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ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III

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Abstract

In this paper, the authors propose a three-layer architecture using an existing planner, which is designed to build a general problemsolving system in a real world. A robot, which has implemented the proposed method, forms the concepts of objects using the Self-Organizing Incremental Neural Network, and then acquires knowledge, online and incrementally, through interaction with the environment or with humans. In addition, it can solve general-purpose problems in a real world by actively working with the various acquired knowledge using the General Problem Solver. In the experiment, the authors show that the proposed method is effective for solving general-purpose problems in a real world using a humanoid robot.