Some Epistemological Problems with the Knowledge Level in Cognitive
  Architectures
  This article addresses an open problem in the area of cognitive systems and
architectures: namely the problem of handling (in terms of processing and
reasoning capabilities) complex knowledge structures that can be at least
plausibly comparable, both in terms of size and of typology of the encoded
information, to the knowledge that humans process daily for executing everyday
activities. Handling a huge amount of knowledge, and selectively retrieve it
ac- cording to the needs emerging in different situational scenarios, is an
important aspect of human intelligence. For this task, in fact, humans adopt a
wide range of heuristics (Gigerenzer and Todd) due to their bounded rationality
(Simon, 1957). In this perspective, one of the re- quirements that should be
considered for the design, the realization and the evaluation of intelligent
cognitively inspired systems should be represented by their ability of
heuristically identify and retrieve, from the general knowledge stored in their
artificial Long Term Memory (LTM), that one which is synthetically and
contextually relevant. This require- ment, however, is often neglected.
Currently, artificial cognitive systems and architectures are not able, de
facto, to deal with complex knowledge structures that can be even slightly
comparable to the knowledge heuris- tically managed by humans. In this paper I
will argue that this is not only a technological problem but also an
epistemological one and I will briefly sketch a proposal for a possible
solution.
