AI: Autonomous Mental Development by Robots and Animals

From: Eugene.Leitl@lrz.uni-muenchen.de
Date: Sun Jan 28 2001 - 12:59:31 MST


http://www.sciencemag.org/cgi/content/full/291/5504/599

ARTIFICIAL INTELLIGENCE: Autonomous Mental Development by Robots and
Animals

Juyang Weng, * James McClelland, Alex Pentland, Olaf Sporns, Ida
Stockman, Mriganka Sur, Esther Thelen

How does one create an intelligent machine? This problem has proven
difficult. Over the past several decades, scientists have taken one of
three approaches: In the first, which is knowledge-based, an
intelligent machine in a laboratory is directly programmed to perform
a given task. In a second, learning-based approach, a computer is
"spoon-fed" human-edited sensory data while the machine is controlled
by a task-specific learning program. Finally, by a "genetic search,"
robots have evolved through generations by the principle of survival
of the fittest, mostly in a computer-simulated virtual world. Although
notable, none of these is powerful enough to lead to machines having
the complex, diverse, and highly integrated capabilities of an adult
brain, such as vision, speech, and language. Nevertheless, these
traditional approaches have served as the incubator for the birth and
growth of a new direction for machine intelligence: autonomous mental
development. As Kuhn wrote (1), "Failure of existing rules is the
prelude to a search for new ones."

A Definition
What is autonomous mental development? With time, a brainlike natural
or an artificial embodied system, under the control of its intrinsic
developmental program (coded in the genes or artificially designed)
develops mental capabilities through autonomous real-time interactions
with its environments (including its own internal environment and
components) by using its own sensors and effectors. Traditionally, a
machine is not autonomous when it develops its skills, but a human is
autonomous throughout its lifelong mental development.

Recent advances in neuroscience illustrate this principle. For
example, if the optic nerves originating from the eyes of an animal
(i.e., a ferret) are connected into the auditory pathway early in
life, the auditory cortex gradually takes on a representation that is
normally found in the visual cortex (2). Further, the "rewired"
animals successfully learn to perform vision tasks with the auditory
cortex. This discovery suggests that the cortex is governed by
developmental principles that work for both visual and auditory
signals. In another example, the developmental program of the monkey
brain dynamically selects sensory input, (e.g., three fingers instead
of one, as normal), according to the actual sensory signal that is
received, and this selection process is active throughout adulthood
(3).

Computational modeling of human neural and cognitive development has
just started to be a subject of study (4, 5). To be successful,
mainstream cognitive psychology needs to advance from explaining
psychological phenomena in specific controlled settings toward
deriving underlying computational principles of mental development
that are applicable to general settings. Such computational studies
are necessary for understanding of mind.

The idea of mental development is also applicable to machines, but it
has not received serious attention in the artificial intelligence
community. In the past, many believed that hand programming alone or
task-specific machine learning could be sufficient for constructing an
intelligent machine. Nevertheless, recently it was pointed out that to
be truly intelligent, machines need autonomous mental development
(6). (See the figure, below.)

Growing up. Mental development is realized through autonomous
interactions with the real physical world.

Manual Versus Autonomous Development
The traditional manual development paradigm can be described as follows:
Start with a task, understood by the human engineer (not the machine).
Design a task-specific representation.
Program for the specific task using the representation.
Run the program on the machine.

If, during program execution, sensory data are used to modify the
parameters of the above predesigned task-specific representation, we
say that this is machine learning. In this traditional paradigm, a
machine cannot do anything beyond the predesigned representation. In
fact, it does not even "know" what it is doing. All it does is run the
program.

The autonomous development paradigm for constructing developmental robots is as
follows:
Design a body according to the robot's ecological working conditions (e.g., on land or
under water).
Design a developmental program.
At "birth," the robot starts to run the developmental program.
To develop its mind, humans mentally "raise" the developmental robot by interacting
with it in real time.

According to this paradigm, robots should be designed to go through a
long period of autonomous mental development, from "infancy" to
"adulthood." The essence of mental development is to enable robots to
autonomously "live" in the world and to become smart on their own,
with some supervision by humans.

Our human genetic program has evolved to use our body
well. Analogously, the developmental programs for robots should also
be body-specific, or specific to robot "species," as are traditional
programs.

However, a developmental program for developing a robot mind must have
other properties (see the table) that set it apart from all the
traditional programs: It cannot be task-specific, because the tasks
are unknown at the time of programming, and the robots should be
enabled to do any job that we can teach them. A human can potentially
learn to take any job--as a computer scientist, an artist, or a
gymnast. The programmer who writes a developmental program for a robot
does not know what tasks the future robot owners will be teaching
it. Furthermore, a developmental program for robots must be able to
generate automatically representations for unknown knowledge and
skills. Like humans and animals, the robots must learn in real time
while performing "on the fly." A mental developmental process is also
an open-ended cumulative process: A robot cannot learn complex skills
successfully without first learning necessary simpler skills, e.g.,
without learning how to hold a pen, the robot will not be able to
learn how to write.

    DIFFERENCES BETWEEN ROBOT PROGRAMS
         Properties
                           Traditional
                                       Developmental
  Not task specific
                           No
                                       Yes
  Tasks are unknown
                           No
                                       Yes
  Generates a representation
  of an unknown task
                           No
                                       Yes
  Animal-like online learning
                           No
                                       Yes
  Open-ended learning
                           No
                                       Yes

Early Prototypes
Early prototypes of developmental robots include Darwin V (7) and SAIL
(6, 8, shown below), developed independently around the same time but
with very different goals. Darwin V was designed to provide a
concrete example for how the computational weights of neural circuits
are determined by the behavioral and environmental interactions of an
autonomous device. Through real-world interactions with physical
objects, Darwin V developed a capability for position-invariant object
recognition, allowing a transition from simple behaviors to more
complex ones.

CREDIT: J. WENG

The goal of the SAIL developmental robot was to generate automatically
representations and architectures for scaling up to more complex
capabilities in unconstrained, unknown human environments. For
example, after a human pushes the SAIL robot "for a walk" along
corridors of a large building, SAIL can navigate on its own in similar
environments while "seeing" with its two video cameras. After humans
show toys to SAIL and help SAIL's hand to reach them, SAIL can pay
attention to these toys, recognize them, and reach them too. To allow
SAIL to learn autonomously, the human robot-sitter lets it explore the
world on its own, but encourages and discourages behaviors by pressing
its "good" button or "bad" button. Responses invariant to
task-unrelated factors are achieved through automatically deriving
discriminating features. A real-time speed is reached by
self-organizing large memory in a coarse-to-fine way (9). These and
other examples that aim at automation of learning [e.g., (10)] have
demonstrated robotic capabilities that have not been achieved before
or that are very difficult to achieve with traditional methods.

The Future
Computational studies of autonomous mental development should be
significantly more tractable than traditional task-specific approaches
to constructing intelligent machines and to understanding natural
intelligence, because the developmental principles are more general in
nature and are simpler than the world around us. For example, the
visual world seen by our eyes is very complex. The light that falls on
a particular pixel in a camera depends on many factors--lighting,
object shape, object surface reflectance, viewing geometry, camera
type, and so on. The developmental principles capture major
statistical characteristics from visual signals (e.g., the mean and
major directions of signal distribution), rather than every aspect of
the world that gives rise to these signals. A task-specific
programmer, in contrast, must study aspects of the world around the
specific task to be learned; this becomes intractable if such a task,
such as vision, speech, or language, requires too many diverse
capabilities.

This new field will provide a unified framework for many cognitive
capabilities--vision, audition, taction, language, planning,
decision-making, and task execution. The sharing of common
developmental principles by visual and auditory sensing modalities, as
recent neuroscience studies have demonstrated, will encourage
scientists to further discover underlying developmental principles
that are shared, not only by different sensing and effector
modalities, but also by different aspects of higher brain
functions. Developmental robots can "live" with us and become smarter
autonomously, under our human supervision.

It is important for neuroscientists and psychologists to discover
computational principles of mental development. And in fact,
developmental mechanisms are quantitative in nature at the level of
neural cells. The precision of knowledge required to verify these
principles on robots will improve our chances of answering some major
open questions in cognitive science, such as how the human brain
develops a sense of the world around it.

Advances in creating robots capable of autonomous mental development
are likely to improve the quality of human life. When robots can
autonomously develop capabilities, such as vision, speech, and
language, humans will be able to train them using their own
communication modes. Developmental robots will learn to perform dull
and repetitive tasks that humans do not like to do, e.g., carrying out
missions in demanding environments such as undersea and space
exploration and cleaning up nuclear waste.

We believe that there is a need for a special program for funding
support of this new field of autonomous mental development. This
program should encourage collaboration among fields that study human
and machine mental development. Biologically motivated mental
development methods for robots and computational modeling of animal
mental development should be especially encouraged. There is also a
need for a multidisciplinary forum for exchanging the latest research
findings in this new field, similar to the Workshop on Development and
Learning funded by NSF and Defense Advanced Research Projects Agency
held at Michigan State University (11). We anticipate a potentially
large impact on science, society, and the economy by advances in this
new direction.

References and Notes

  1.T. S. Kuhn, The Structure of Scientific Revolution (Univ. of Chicago Press,
     Chicago, 3rd ed., 1996), p. 68.
  2.L. von Melchner, S. L. Pallas, M. Sur, Nature 404, 871 (2000).
  3.X. Wang, M. M. Merzenich, K. Sameshima, W. M. Jenkins, Nature 378, 13 (1995).
  4.J. L. Elman et al., Rethinking Innateness: A Connectionist Perspective on
     Development (MIT Press, Cambridge, MA, 1997).
  5.E. Thelen, E. G. Schoner, C Scheier, L. B. Smith, Behav. Brain Sci., in press.
  6.J. Weng, in Learning in Computer Vision and Beyond: Development in Visual
     Communication and Image Processing, C. W. Chen, Y. Q. Zhang, Eds. (Marcel
     Dekker, New York, 1998) (Michigan State Univ. tech. rep. CPS 96-60, East
     Lansing, MI, 1996).
  7.N. Almassy, G. M. Edelman, O. Sporns, Cereb. Cortex 8, 346 (1998).
  8.J. Weng, W. S. Hwang, Y. Zhang, C. Evans, in Proceedings of the 2nd
     International Symposium on Humanoid Robots, 8 to 9 October 1999, Tokyo, pp.
     57-64.
  9.W. S. Hwang, J. Weng, IEE Trans. Pattern Anal. Machine Intell. 22, 11 (2000).
 10.D. Roy, B. Schiele, A. Pentland, in Workshop on Integrating Speech and Image
     Understanding, Proceedings of an International Conference on Computer Vision,
     21 September 1999, Corfu, Greece (IEEE Press, New York, 1999).
 11.Proceedings of Workshop on Development and Learning, 5 to 7 April 2000,
     Michigan State University, East Lansing, MI. www.cse.msu.edu/dl/.

J. Weng is at the Department of Computer Science and Engineering,
Michigan State University, East Lansing, MI 48824, USA. J. McClelland
is at the Center for the Neural Basis of Cognition, Carnegie Mellon
University, Pittsburgh, PA 15213, USA. A. Pentland is at The Media
Laboratory, Massachusetts Institute of Technology, Cambridge, MA
02139, USA. O. Sporns and E. Thelen are at the Department of
Psychology, Indiana University, Bloomington, IN 47405,
USA. I. Stockman is at the Department of Audiology and Speech
Sciences, Michigan State University, East Lansing, MI 48824,
USA. M. Sur is at the Department of Brain and Cognitive Sciences,
Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

*To whom correspondence should be addressed. E-mail: weng@cse.msu.edu



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