Incremental acquisition of knowledge for non-monotonic reasoning.
Computers and Artificial Intelligence, 13(4):377--396,
1994.
Abstract:
The use of conventional non-monotonic reasoning tools in real-sized knowledge-based
applications is hindered by the fact that the knowledge acquisition phase cannot
be accomplished in the incremental way that is instead typical of knowledge base
management systems based on monotonic logics. As a result, some researchers have
departed from orthodox non-monotonic formalisms and proposed languages for the representation
of Multiple Inheritance Networks with Exceptions (MINEs). Such languages do
not suffer from the problem of incrementality in knowledge acquisition, but are inadequate
both from a formal and from an empirical point of view. In fact, they are not endowed
with a formal semantics, and the intuitions that underlie their inferential mechanisms
are far from being widely agreed upon. In this paper we discuss an approach to non-monotonic
reasoning which does allow the phase of knowledge acquisition to be accomplished
in an incremental and modular way, but at the same time relies on a solid and widely
acknowledged formal apparatus such as First Order Logic (FOL). We have obtained this
by specifying a (non-monotonic) function that maps MINEs into sets of FOL formulae.
We have shown that the mapping function we discuss is sound and complete, in the
sense that each conclusion that can be derived from a MINE is also derivable from
the set of FOL formulae resulting from its translation via the mapping function,
and viceversa.