Page 42 - Rapid Learning in Robotics

28 Artificial Neural Networks

Consider the case of a network, which is already well trained with
the data set A. When a new data set B gets available, the knowledge
about “skill” A can be deteriorated (interference) mainly in the fol-
lowing ways:

(i) due to re-allocation of the computational resources to new map-
ping domains the old skill (A) becomes less accurate (“stability – plas-
ticity” problem).
(ii) Further data sets A and B might be inconsistent due to a change
in the mapping task and require a re-adaptation.

(iii) Beyond these two principal, problem-immanent interferences, a
global learning process can cause “catastrophic interference”: when
the weight update to new data is global, it is hard to control, how
this influences knowledge previously learned. A popular solution is
to memorize the old dataset A, and retrain the network based on the
merged dataset A and B.
One of the main challenges in on-line learning is the proper control
of the current context. It is crucial in order to avoid wrong general-
ization for other contexts - analog to the human “traumatic experi-
ences” (see also localized representations above, mixture-of-experts
below and Chap. 9 for the problem of context oriented learning).

Fixed versus adaptable network structures As pointed out before, the suit-
able network (model) structure has significant influence on the effi-
ciency and performance of the learning system. Several methods
have been proposed for tackling the combined problem of adapt-
ing the network weights and dynamically deciding on the structural
adaptation (e.g. growth) of the network (additive models). Strategies
on selecting the network size will be later discussed in Sec. 3.6.

For a more complete overview of the field of neural networks we refer
the reader to the literature, e.g. (Anderson and E. Rosenfeld 1988; Hertz,
Krogh, and Palmer 1991; Ritter, Martinetz, and Schulten 1992; Arbib 1995).

3.3 Learning as Approximation Problem

In this section learning tasks are considered from the perspective of basic
representation types and their relation to methods of other disciplines.