The field of AI is rich in scientific and technical challenges. Progress need to be made in machine learning paradigms to make them more efficient and less data intensive. Bridges between data-based and model-based AI are needed in order to benefit from the best of both approaches. Many real-life situations cannot yet be addressed by current robots, demanding progress in perception, scene interpretation or group coordination. This chapter addresses some of the major scientific and technological challenges in core AI technology.
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