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@phdthesis{Rig98-PT,
author = {Fabrizio Riguzzi},
title = {Extensions of Logic Programming as a Representation Language
for Machine Learning},
school = {DEIS,
Universit\`{a} of Bologna},
year = {1998},
month = nov,
note = {Technical Report DEIS-LIA-98-005, LIA Series n.33},
url = {http://www-lia.deis.unibo.it/Research/TechReport/lia98005.zip},
pdf = {http://mcs.unife.it/~friguzzi/Papers/Rig-PT98.pdf},
address = {Bologna, \Italy},
keywords = {Abduction, Integrity Constraints, Knowledge Discovery,
Multiple Predicate Learning, Negation},
abstract = {The representation language of Machine Learning has undergone a substantial
evolution, starting from numerical descriptions to an
attribute-value representations and finally to first order logic
languages. In particular, Logic Programming has recently been
studied as a representation language for learning in the research
area of Inductive Logic Programming. The contribution of this
thesis is twofold. First, we identify two problems of existing
Inductive Logic Programming techniques: their limited ability to
learn from an incomplete background knowledge and the use of a
two-valued logic that does not allow to consider some pieces of
information as unknown. Second, we overcome these limits by
prosecuting the general trend in Machine Learning of increasing
the expressiveness of the representation language. Two learning
systems have been developed that represent knowledge using two
extensions of Logic Programming, namely abductive logic programs
and extended logic programs.
Abductive logic programs allow abductive reasoning to be
performed on the knowledge. When dealing with an incomplete
knowledge, abductive reasoning can be used to explain an
observation or a goal by making some assumptions about
incompletely specified predicates. The adoption of abductive logic
programs as a representation language for learning allows to
learn from an incomplete background knowledge: abductive
reasoning is used during learning for completing the available
knowledge. The system ACL (Abductive Concept Learning) for
learning abductive logic programs has been implemented and tested
on a number of datasets. The experiments show that the
performance of the system when learning from incomplete knowledge
are superior or comparable to those of ICL-Sat, mFOIL and FOIL.
Extended logic programs contain a second form of negation (called
explicit negation) besides negation by default. They allow the
adoption of a three-valued model and the representation of both
the target concept and its opposite. The two-valued setting that
is usually adopted in Inductive Logic Programming can be a
limitation in some cases, for example in the case of a robot that
autonomously explores the surrounding world and that acts on the
basis of the partial knowledge it posseses. For such a robot is
important to distinguish what is true from what is false and what
is unknown and therefore it needs to adopt a three-valued logic.
The system LIVE (Learning In a three-Valued Environment) has been
implemented that is able to learn extended logic programs
containing a definition for both the concept and its opposite.
Moreover, the definitions learned may allow exceptions. In this
case, a definition for the class of exceptions is learned and for
exceptions to exceptions, if present. In this way, hierarchies of
exceptions can be learned.},
copyright = {Fabrizio Riguzzi}
}
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