Integration of Learning into a Knowledge Modeling Framework
Enric Plaza & Josep Lluís Arcos
IIIA - Institut d'Investigació en
Intel.ligència Artificial,
CSIC - Spanish Scientific Research Council,
Campus Universitat Autonòma de Barcelona,
08193 Bellaterra, Catalonia, Spain.
Voice: +34 3 5809570;
Fax: +34 3 5809661;
Email: plaza@iiia.csic.es & arcos@iiia.csic.es
WWW: http://www.iiia.csic.es
In this paper we will report our current research on the NOOS language, an attempt to
provide a uniform representation framework for inference and learning components supporting
flexible and multiple combination of these components. Rather than a specific combination of
learning methods, we are interested in an architecture adaptable to different domains where multiple
learning strategies (combinations of learning methods) can be programmed. Our approach derives
from the knowledge modelling frameworks developed for the design and construction of KBSs
based on the task/method decomposition principle and the analysis of knowledge requirements for
methods. Our thesis is that learning methods are methods with introspection capabilities that can
be also analyzed in the same task/method decomposition. In order to infer new decisions from the
results and behavior of other inference processes, those results and behavior have to be represented
and stored in the memory for the learning method to be able to work with them.