Abstract
In this paper, the authors propose a three-layer architecture using an existing planner, which is designed to build a general problem-solving 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.
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Makibuchi, N., Shen, F., Hasegawa, O. (2010). Online Knowledge Acquisition and General Problem Solving in a Real World by Humanoid Robots. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_76
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DOI: https://doi.org/10.1007/978-3-642-15825-4_76
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