@inproceedings{LamMelRig98-PAP98-IC,
author = { Evelina Lamma and Paola Mello and Fabrizio Riguzzi},
title = {A System for Measuring Function Points},
booktitle = {Proceedings of the 6th International Conference on Practical Applications
of Prolog and 4th International Conference on Practical Applications of Constraint Technology
(PAPPACT98), London, March 1998},
publisher = {The Practical Application Company Ltd.},
address = {London, \UK},
year = 1998,
pages = {41--60},
month = mar,
pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamMelRig-PAP98.pdf},
abstract = {We present the system FUN for measuring the Function Point software metric from specifications expressed in the form of an Entity Relationship (ER) diagram and a Data Flow Diagram (DFD). As a first step towards the implementation of the system, the informal Function Point counting rules have been translated into rigorous rules expressing properties of the ERDFD. Prolog was chosen for the implementation because of its declarativity and maintainability. Thanks to its relational representation, it was possible to directly represent the input ER-DFD with Prolog facts. Declarativity allowed a straightforward translation of the rigorous rules into code and a quick implementation of a working prototype. Finally, maintainability was a primary concern since the Function Point counting method is continually evolving.},
keywords = {Function Points, Software Metrics, Logic Programming.}
}
@inproceedings{LamRigPer98-LPNMR98-IW,
author = {Evelina Lamma and Fabrizio Riguzzi and Lu\'{i}s Moniz Pereira},
title = {Learning with Extended Logic Programs},
booktitle = {Proceedings of the Logic Programming track
of the Seventh International Workshop on Nonmonotonic Reasoning ({LP-NMR98}), Trento, Italy,
May 30 - June 1, 1998},
year = 1998,
editor = {Juergen Dix and Jorge Lobo},
month = may,
publisher = {Universit\"at Koblenz\--Landau, Institut f\"ur Informatik},
pages = {1--9},
address = {Koblenz, \Germany},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamRigPer98-LPNMR98.pdf},
url = {http://www.cs.man.ac.uk/~jdix/NMR7/SUBMISSIONS/riguzzi.ps.gz},
abstract = {We discuss the adoption of a three-valued setting for inductive concept
learning. Distinguishing between what is true, what is false and what is
unknown can be useful in situations where decisions have to be taken on the
basis of scarce information. In a three-valued setting, we want to learn a
definition for both the target concept and its opposite, considering
positive and negative examples as instances of two disjoint classes. To
this purpose, we adopt extended logic programs under a well-founded
semantics as the representation formalism for learning. In this way, we are
able to represent both the concept and its opposite and deal with
incomplete or unknown information.
We discuss various approaches to be adopted in order to handle possible
inconsistencies. Default negation is used to ensure consistency and to
handle exceptions to general rules. Exceptions to a positive concept are
identified from negative examples, whereas exceptions to a negative concept
are identified from positive examples. Exceptions can be generalized, in
their turn, by learning within a hierarchy of defaults.},
keywords = {Inductive Logic Programming, Extended Logic Programs}
}
@inproceedings{LamRigPer98-MSL98-IW,
author = {Evelina Lamma and Fabrizio Riguzzi and Lu\'{i}s Moniz Pereira},
title = {Strategies for Learning with
Extended Logic Programs},
booktitle = {Proceedings of the Fourth International Workshop on
Multistrategy Learning ({MSL98}), Desenzano del Garda, Italy, 11--13 June 1998},
year = 1998,
publisher = {Dipartimento di Informatica, Universit\`{a} di Torino},
month = jun,
address = {Torino, \Italy},
pages = {99--108},
editor = {Floriana Esposito and Ryszard Michalski and Lorenza Saitta}
}
@inproceedings{Rig98-COMPULOG98-IW,
author = {Fabrizio Riguzzi},
title = {Learning in a Three-valued Setting},
booktitle = {Proceedings of the CompulogNet Area Meeting ``Computational
Logic and Machine Learning'', June 1998, Bristol, UK},
year = 1998,
month = jun,
pages = {63--69},
publisher = {University of Manchester},
address = {Manchester, \UK},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/Rig98-COMPULOG98.pdf},
abstract = {We discuss the adoption of a three-valued setting for inductive concept
learning. Distinguishing between what is true, what is false and what is
unknown is necessary in situations where decisions have to be taken on the
basis of scarce information. We propose a learning algorithm that adopts
extended logic programs under a well-founded semantics as the
representation formalism and learns a definition for both the target
concept and its opposite, considering positive and negative examples as
instances of two disjoint classes.
In the target program, default negation is used to ensure consistency and
to handle exceptions to general rules. Exceptions to a positive concept are
identified from negative examples, whereas exceptions to a negative concept
are identified from positive examples. Exceptions can be generalized, in
their turn, resulting in a hierarchy of defaults.
},
keywords = {Inductive Logic Programming, Extended Logic Programs}
}
@inproceedings{LamMelMil98-LPKR97-IC,
author = {Evelina Lamma AND Paola Mello AND Michela Milano AND Fabrizio Riguzzi},
title = {A System for Abductive Learning of Logic
Programs},
booktitle = {Logic Programming and Knowledge Representation:
Third International Workshop, {LPKR}'97, Port Jefferson, New York,
USA, October 17, 1997. Selected Papers},
editor = {J. Dix and L. M. Pereira and T. Przymusinski},
publisher = {Springer Verlag},
address = {Hidelberg, \Germany},
year = 1998,
month = jun,
series = {Lecture Notes on Computer Science},
volume = {1471},
abstract = {We present the system LAP (Learning Abductive Programs)
that is able to learn abductive logic programs from examples and from a
background abductive theory. A new type of induction problem has been
dened as an extension of the Inductive Logic Programming framework.
In the new problem denition, both the background and the target the-
ories are abductive logic programs and abductive derivability is used as
the coverage relation.
LAP is based on the basic top-down ILP algorithm that has been suit-
ably extended. In particular, coverage of examples is tested by using the
abductive proof procedure dened by Kakas and Mancarella [24]. As-
sumptions can be made in order to cover positive examples and to avoid
the coverage of negative ones, and these assumptions can be used as
new training data. LAP can be applied for learning in the presence of
incomplete knowledge and for learning exceptions to classication rules.},
scopus = {2-s2.0-84867834356},
keywords = {Abduction; Negation; Integrity Constraints},
note = {The original publication is available at \url{http://www.springerlink.com}},
doi = {10.1007/BFb0054787},
keywords = {Abduction, Negation, Integrity Constraints},
issn = {0302-9743},
pages = {102--122},
url = {http://link.springer.com/chapter/10.1007%2FBFb0054792},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamMelMil-LPKR97.pdf},
copyright = {Springer},
abstract = {We present the system LAP (Learning Abductive Programs)
that is able to learn abductive logic programs from examples and from a
background abductive theory. A new type of induction problem has been
de ned as an extension of the Inductive Logic Programming framework.
In the new problem de nition, both the background and the target the-
ories are abductive logic programs and abductive derivability is used as
the coverage relation.
LAP is based on the basic top-down ILP algorithm that has been suit-
ably extended. In particular, coverage of examples is tested by using the
abductive proof procedure de ned by Kakas and Mancarella [24]. As-
sumptions can be made in order to cover positive examples and to avoid
the coverage of negative ones, and these assumptions can be used as
new training data. LAP can be applied for learning in the presence of
incomplete knowledge and for learning exceptions to classi cation rules.}
}
@inproceedings{Rig98-ECAI98-IC,
author = {Fabrizio ~Riguzzi},
title = {Integrating Abduction and Induction},
booktitle = {Proceedings of the 13th European Conference on Artificial
Intelligence ({ECAI98}), Brighton, UK, August 23--28 1998},
editor = {Henri Prade},
publisher = {John Wiley and Sons},
address = {Chichester, \UK},
year = 1998,
month = aug,
pages = {475--476},
keywords = {Abduction, Negation, Integrity Constraints},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/Rig-ECAI98.pdf},
wos = {WOS:000085168300114},
isbn = {0471984310}
}
@inproceedings{KakLamRig98-AIMSA98-IC,
author = { Antonis Kakas and Evelina Lamma and Fabrizio Riguzzi},
title = {Learning Multiple Predicates},
booktitle = {Artificial Intelligence: Methodology, Systems, and Applications:
8th International Conference, {AIMSA}'98, Sozopol,
Bulgaria, September 21-23, 1998. Proceedings},
series = {Lecture Notes on Artificial Intelligence},
volume = {1480},
note = {The original publication is available at \url{http://www.springerlink.com}},
publisher = {Springer Verlag},
address = {Heidelberg, \Germany},
year = 1998,
month = sep,
pages = {303--316},
keywords = {Abduction, Multiple Predicate Learning},
issn = {0302-9743},
isbn = {354064993X},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/KakLamRig-AIMSA98.pdf},
url = {http://link.springer.com/chapter/10.1007%2FBFb0057454},
doi = {10.1007/BFb0057454},
copyright = {Springer},
abstract = {We present an approach for solving some of the problems of
top-down Inductive Logic Programming systems when learning multiple predicates.
The approach is based on an algorithm for learning abductive logic programs.
Abduction is used to generate additional information that is useful
for solving the problem of global inconsistency when learning
multiple predicates.},
scopus = {2s2.084867758281},
wos = {WOS:000083673700025}
}
@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://ml.unife.it/wp-content/uploads/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}
}
@article{Rig98a-NJ,
author = {Fabrizio Riguzzi},
title = {Apprendimento a {ECAI}98},
journal = {AI*IA Notizie (Periodico dell'Associazione Italiana per l'Intelligenza
Artificiale)},
year = {1998},
volume = {Anno {XI}},
number = {4},
month = dec,
pages = {32--34},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/Rig-AIIANotizie98a.pdf}
}
@article{Rig98b-NJ,
author = {Fabrizio Riguzzi},
title = {Abstract della tesi di dottorato:
Estensioni del linguaggio di rappresentazione della programmazione logica induttiva},
journal = {AI*IA Notizie (Periodico dell'Associazione Italiana per l'Intelligenza
Artificiale)},
year = {1998},
volume = {Anno {XI}},
number = {4},
month = dec,
pages = {48--48},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/Rig-AIIANotizie98b.pdf}
}
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