Autonomous Mental Development by Robots and Animals

8 November, 2010 § Leave a comment

Autonomous Mental Development by Robots and Animals”[1] was one of the first publications to introduce a new field of artificial intelligence research, called Autonomous Mental Development (AMD), to the mainstream scientific research community. The paper was written after the first Workshop on Development and Learning took place at Michigan State University in 2000.

The authors of the article differentiate AMD from traditional artificial intelligence such as knowledge-based, learning-based, or genetic-search based approaches. The authors state that the difference between traditional and developmental robotic programming is based on five factors: task specificity; awareness of tasks at time of programming; representation of unknown task; animal-like online learning; and open-ended learning. While AMD differs from more traditional approaches in at least these six ways, it is the combination of these six factors that defines AMD. Up to the point of the publication, initial progress in the field allowed robots to learn objects and places that they had interacted with.

Researchers working on problems of AMD are trying to build programs that understand and know what they are doing as opposed to programs that are simply following instruction that the human programmer has provided. This separation has been called strong vs. weak AI, respectively. I think the article could have benefited by acknowledging the difference since the target audience was that of a mainstream science publication and not one solely for the artificial intelligence community.

One of the figures in the article describes how developmental programs in a machine brain are similar to those of a human brain. Specifically, the figure shows the human development starting in the genes and leading to an adult brain. The growth of this human brain is affected by the environment that it lives in, and the environment of its ancestors through the inheritance of special genetic traits. Further, the article states that humans “raise the developmental robot by interacting with it in real time”. While this goal would have to be accomplished, it leaves out the necessity for previous generations of developmental programs to pass on to their offspring what helped them to be successful.

The first research published in the area of AMD[2] covered facial recognition, feature selection, feature extraction, and self-organization. Since research has started, many questions have been raised as to how systems that are fully-autonomous, task-nonspecific, and open-ended could be created, and the problem space still has areas that are not well defined.

This article provides a good introduction to AMD and the problems the field is trying to solve. To this day, interest in AMD has continued to grow, as shown by the creation of the IEEE CIS AMD Newsletter, started in 2004, and the IEEE Transactions on Autonomous Mental Development journal, started in 2009. Hopefully we will see a day where machines can learn new tasks and correct their mistakes.

[1] Weng et al. “Autonomous Mental Development by Robots and Animals”. Science Magazine. Volume 291, Number 5504, Issue of 26 Jan 2001, pp. 599-60.
[2] Weng et al. “Learning Recognition and Segmentation Using the Cresceptron”. International Journal of Computer Vision. Volume 25, Number 2, Issue of Nov 1997, pp. 105-139.

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