"Perspectives
There are at least three broad perspectives one can lend to the field of knowledge
representation and reasoning (KRR). It is likely that all three perspectives will be
presented, regardless of the over all thematic organization of the course.
KR as applied epistemology
All intelligent activity presupposes knowledge. Knowledge is represented in a knowledge
base, which consists of knowledge structures (typically symbolic) and programs.
Brian C. Smith's Knowledge Representation Hypothesis forms the basis of this
perspective (see References and Resources below): NL_Brian
C. Smith http://www.mkp.com/books_catalog/01xtoc.htm
Any mechanically embodied intelligent process will be comprised of structural ingredients
that we as external observers naturally take to represent a propositional account of
the knowledge that the overall process exhibits, and} independent of such external
semantical attribution, play a formal but causal and essential role in engendering the
behavior that manifests that knowledge.
KR as a tell-ask module:
Not necessarily exclusive from the above, this is the lowest expectation out of a
knowledge representation module in any AI system. Any KR system should provide at least
two operations:
TELL(K,f) Given a knowledge base K, the fact fis added to it resulting in a new knowledge
base, say K'.
ASK(K, f)The knowledge base K is being queried about a fact f. The answer, depending upon
the KR paradigm (see below) used, may be yes, no, unknown, yes with a confidence factor of
A, ...etc.
KR as the embodiment of AI systems
This is the connectionist view. This approach takes the view that there are several
(perhaps millions!) identical interconnected units that are collectively responsible for
representing various concepts. A concept is represented in a distributed sense (as opposed
to local) and is indicated by an evolving pattern of activity over a collection of units. "
See: http://blackcat.brynmawr.edu/~dkumar/UGAI/kr.html
