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Abstract One of the most critical aspects of a truly intelligent system is the ability to learn, that is, to improve its own functionality by interacting with the environment and exploring it. In this paper, we argue that learning from exploring the environment should be the main goal in developing artificial intelligence. We also argue in favor of an integrated system—combining several state‐of‐the‐art aspects of artificial intelligence, such as speech, vision, natural language, expert systems—as the experimental platform with which to approach this problem. We then describe the main features of a project of this type, MAIA, which is under development at I.R.S.T. The vision components of the system will be discussed in some detail, especially the navigation architecture of the indoor robot available to MAIA. We will conclude outlining some initial learning problems that will be approached within the MAIA project, such as learning to recognize faces and learning to update the map of the Institute used for indoor navigation.
Poggio et al. (Wed,) studied this question.