Page 17 - Rapid Learning in Robotics

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plinary field of researchers from physiology, neuro-biology, cognitive and
computer science. Physics contributed methods to deal with systems con-
stituted by an extremely large number of interacting elements, like in a
ferromagnet. Since the human brain contains of about neurons with
interconnections and shows a — to a certain extent — homogeneous
structure, stochastic physics (in particular the Hopfield model) also en-
larged the views of neuroscience.
Beyond the phenomenon of “learning”, the rapidly increasing achieve-
ments that became possible by the computer also forced us to re-think
about the before unproblematic phenomena “machine” and “intelligence”.
Our ideas about the notions “body” and “mind” became enriched by the
relation to the dualism of “hardware” and “software”.
With the appearance of the computer, a new modeling paradigm came
into the foreground and led to the research field of artificial intelligence.It
takes the digital computer as a prototype and tries to model mental func-
tions as processes, which manipulate symbols following logical rules –
here fully decoupled from any biological substrate. Goal is the develop-
ment of algorithms which emulate cognitive functions, especially human
intelligence. Prominent examples are chess, or solving algebraic equa-
tions, both of which require of humans considerable mental effort.
In particular the call for practical applications revealed the limitations
of traditional computer hardware and software concepts. Remarkably, tra-
ditional computer systems solve tasks, which are distinctively hard for
humans, but fail to solve tasks, which appear “effortless” in our daily life,
e.g. listening, watching, talking, walking in the forest, or steering a car.
This appears related to the fundamental differences in the information
processing architectures of brains and computers, and caused the renais-
sance of the field of connectionist research. Based on the von-Neumann-
architecture, today computers usually employ one, or a small number of
central processors, working with high speed, and following a sequential
program. Nevertheless, the tremendous growth in availability of cost-
efficiency computing power enables to conveniently investigate also par-
allel computation strategies in simulation on sequential computers.
Often learning mechanisms are explored in computer simulations, but
studying learning in a complex environment has severe limitations - when
it comes to action. As soon as learning involves responses, acting on, or
inter-acting with the environment, simulation becomes too easily unreal-