@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{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{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}
}
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