annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:341634",
rights = "unrestricted",
size = "8 pages",
abstract = "Knowledge representation is a key issue for any
machine learning task. There have already been many
comparative studies about knowledge representation with
respect to machine learning in classification tasks.
However, apart from some work done on reinforcement
learning techniques in relation to state
representation, very few studies have concentrated on
the effect of knowledge representation for machine
learning applied to problem solving, and more
specifically, to planning. In this paper, we present an
experimental comparative study of the effect of
changing the input representation of planning domain
knowledge on control knowledge learning. We show
results in two classical domains using three different
machine learning systems, that have previously shown
their effectiveness on learning planning control
knowledge: a pure EBL mechanism, a combination of EBL
and induction (HAMLET), and a Genetic Programming based
system (EVOCK).",
notes = "Also known as
\cite{Aler:2000:KRI:645529.657964}