http://www.sciencedaily.com/releases/2008/03/080329122121.htm
http://cordis.europa.eu/ictresults/index.cfm/section/news/tpl/article/BrowsingType/Features/ID/89632
New Breed Of Cognitive Robot Is A Lot Like A Puppy
ScienceDaily (Mar. 31, 2008) - Designers of artificial cognitive systems
have tended to adopt one of two approaches to building robots that can think
for themselves: classical rule-based artificial intelligence or artificial
neural networks. Both have advantages and disadvantages, and combining the
two offers the best of both worlds, say a team of European researchers who
have developed a new breed of cognitive, learning robot that goes beyond the
state of the art.
he researchers' work brings together the two distinct but mutually
supportive technologies that have been used to develop artificial cognitive
systems (ACS) for different purposes. The classical approach to artificial
intelligence (AI) relies on a rule-based system in which the designer
largely supplies the knowledge and scene representations, making the robot
follow a decision-making process - much like climbing through the branches
of a tree - toward a predefined response.
Biologically inspired artificial neural networks (ANNs), on the other hand,
rely on processing continuous signals and a non-linear optimisation process
to reach a response which, due to the lack of preset rules, requires
developers to carefully balance the system constraints and its freedom to
act autonomously.
"Developing systems in classical AI is essentially a top-down approach,
whereas in ANN it is a bottom-up approach," explains Michael Felsberg, a
researcher at the Computer Vision Laboratory of Linköping University in
Sweden. "The problem is that, used individually, these systems have major
shortcomings when it comes to developing advanced ACS architectures. ANN is
too trivial to solve complex tasks, while classical AI cannot solve them if
it has not been pre-programmed to do so."
Beyond the state of the art
Working in the EU-funded COSPAL project, Felsberg's team found that using
the two technologies together solves many of those issues. In what the
researchers believe to be the most advanced example of such a system
developed anywhere in the world, they used ANN to handle the low-level
functions based on the visual input their robots received and then employed
classical AI on top of that in a supervisory function.
"In this way, we found it was possible for the robots to explore the world
around them through direct interaction, create ways to act in it and then
control their actions in accordance. This combines the advantages of
classical AI, which is superior when it comes to functions akin to human
rationality, and the advantages of ANN, which is superior at performing
tasks for which humans would use their subconscious, things like basic motor
skills and low-level cognitive tasks," notes Felsberg.
The most important difference between the COSPAL approach and what had been
the state of the art is that the researchers' ACS is scalable. It is able to
learn by itself and can solve increasingly complex tasks with no additional
programming.
"There is a direct mapping from the visual precepts to performing the
action," Felsberg confirms. "With previous systems, if something in the
environment changed that the low-level system was not programmed to
recognise, it would give random responses but the supervising AI process
would not realise anything was wrong. With our approach, the system realises
something is different and if its actions do not result in success it tries
something else," the project coordinator explains.
"Like training a child or a puppy"
This trial-and-error learning approach was tested by making the COSPAL robot
complete a shape-sorting puzzle, but without telling it what it had to do.
As it tried to fit pegs into holes it gradually learnt what would fit where,
allowing it to complete the puzzle more quickly and accurately each time.
"After visual bootstrapping, the only human input was from an operator who
had two buttons, one to tell the robot it was successful and another to tell
it that it had made a mistake. It is much like training a child or a puppy,"
Felsberg says.
Though a learning, cognitive robot of the kind developed in COSPAL
constitutes an important leap forward toward the development of more
autonomous robots, Felsberg says it will be some time before robots gain
anything close to human cognition and intelligence, if they ever do.
"In human terms, our robot is probably like a two or three year old child,
and it will take a long time for the technology to progress into the
equivalent of adulthood. I don't think we will see it in our lifetimes," he
says.
Nonetheless, robots like those developed in COSPAL will undoubtedly start to
play a greater role in our lives. The project partners are in the process of
launching a follow-up project called DIPLECS to test their ACS architecture
in a car. It will be used to make the vehicle cognitive and aware of its
surroundings, creating an artificial co-pilot to increase safety no matter
the weather, road or traffic conditions.
"In the real world you need a system that is capable of adapting to
unforeseen circumstances, and that is the greatest accomplishment of our
ACS," Felsberg notes.
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agi
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