Abstract
"Explanation-based" learning is a semantically-driven, knowledge-intensive
paradigm for machine learning which contrasts sharply with syntactic or
"similarity-based" approaches. This paper redevelops the
foundations of EBL from the perspective of problem-solving. Viewed in this
light, the technique is revealed as a simple modification to an
inference engine which gives it the ability to generalize the conditions
under which the solution to a particular problem holds. We show how
to embed generalization invisibly within the problem solver, so that it is
accomplished as inference proceeds rather than as a separate step. The
approach is also extended to the more complex domain of planning, which
involves maintaining and operating on a global world state, to illustrate
that it is by no means restricted to toy problem-solvers. We argue against
the current trend to isolate learning from other activity and study it
separately, preferring instead to integrate it into the very heart of
problem solving.
Notes
We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at digitize@ucalgary.ca