1997.bib

@inproceedings{LamMelMil97-JCIS97-IC,
  author = {Evelina Lamma AND Paola Mello AND Michela Milano AND Fabrizio Riguzzi},
  title = {Integrating Induction and Abduction in Logic
Programming},
  booktitle = {Proceedings of the Third Joint Conference on Information
         Sciences, 1--5 March 1997, Raleigh, North Carolina},
  editor = {Paul P. Wang},
  pages = {203--206},
  year = 1997,
  publisher = {Duke University},
  volume = {2},
  address = {Research Triangle Park, North Carolina, \USA},
  month = mar,
  keywords = {Abduction, Negation, Integrity Constraints},
  isbn = {0-9643456-5-x},
  abstract = {We propose an approach for the integration of induc-
tive and abductive reasoning.},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/LamMelMil-JCIS97.pdf}
}
@techreport{GraLamMel97-TR,
  author = {Fausto Gramantieri and Evelina Lamma and Paola Mello and Fabrizio Riguzzi},
  title = {A System for Measuring Function Points from Specifications},
  institution = {DEIS, Universit\`{a} di Bologna},
  year = 1997,
  number = {DEIS-LIA-97-006,  LIA Series n.23},
  month = apr,
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/GraLamMel-TR97.pdf}
}
@inproceedings{GraLamMel97-NW,
  author = {Fausto Gramantieri and Evelina Lamma and Paola Mello and Fabrizio Riguzzi},
  title = {Un Sistema Basato sulla Conoscenza per il Calcolo dei Function Point},
  booktitle = {Incontro del Gruppo di Lavoro su Rapprensentazione della Conoscenza e
    Ragionamento Automatico dell'Associazione Italiana per l'Intelligenza Artificiale (AI*IA)
    e dell'Associazione italiana Tecnologie Avanzate Basate su concetti Orientati ad Oggetti
    (TABOO) dal titolo ``Rappresentazione della conoscenza e
     tecniche ad oggetti nell'ingegneria del software'', Bologna, 4 \April\ 1997},
  month = apr,
  year = 1997,
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/GraLamMel-TABOO97.pdf}
}
@inproceedings{LamMelMil97-GULP97-IC,
  author = {Evelina ~Lamma AND Paola ~Mello AND Michela ~Milano AND Fabrizio
   ~Riguzzi},
  title = {An Algorithm for Learning Abductive Rules},
  booktitle = {Proceedings of the APPIA-GULP-PRODE 97 Joint Conference on
        Declarative Programming, Grado, Italy, 16--19 June 1997},
  editor = {Moreno Falaschi and Marisa Navarro and Alberto Policriti},
  year = 1997,
  month = jun,
  pages = {295--305},
  publisher = {Dipartimento di Matematica e Informatica, Universit\`a di Udine and Gruppo
   Ricercatore e Utenti di Logic Programming},
  keywords = {Abductive Logic Programming. Inductive Logic Programming},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/LamMelMil-GULP97.pdf},
  abstract = {We propose an algorithm for learning abductive logic programs from examples.
We consider the Abductive Concept Learning framework, an extension of the Induc-
tive Logic Programming framework in which both the background and the target
theories are abductive logic programs and the coverage of examples is replaced by
abductive coverage. The two main benets of this integration are the increased
expressive power of the background and target theories and the possibility of learn-
ing in presence of incomplete knowledge. We show that the algorithm is able to
learn abductive rules and we present an application of the algorithm to a learning
problem in which the background knowledge is incomplete.}
}
@inproceedings{LamMelMil97-LOPSTR97-IW,
  author = {Evelina ~Lamma AND Paola ~Mello AND Michela ~Milano AND Fabrizio
   ~Riguzzi},
  title = {Integrating Extensional and Intensional {ILP} Systems through
Abduction},
  editor = {Norbert E. Fuchs},
  booktitle = {{LOPSTR97}, Proceedings of the 7th International Workshop on Logic Program
         Synthesis and Transformation, Leuven, Belgium, July 10-12, 1997},
  year = 1997,
  keywords = {Abduction, Negation, Integrity Constraints, Inductive Logic Programming},
  month = jul,
  address = {Leuven, \Belgium},
  pages = {1--8},
  publisher = {Department of Computer Science, Katholieke Universiteit Leuven},
  volume = {Report CW 253},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/LamMelMil-LOPSTR97.pdf},
  abstract = {We present an hybrid extensional-intensional Inductive Logic Programming
algorithm. We then show how this algorithm solves the problem of global
inconsistency of intensional systems when learning multiple predicates,
without incurring in the problems of incompleteness and inconsistency of
extensional systems. The algorithm is obtained by modifying an intensional
system for learning abductive logic programs.
Extensionality is thus obtained by exploiting abduction: the training set
is considered as a set of abduced literals that is taken as input by the
abductive proof procedure used for the coverage of examples.
}
}
@inproceedings{KakRigu97-ILP97-IC,
  author = {Antonis C. Kakas AND Fabrizio Riguzzi},
  title = {Learning with Abduction},
  booktitle = {Inductive Logic Programming: 7th International Workshop, {ILP}-97 Prague, Czech Republic, September 17-20, 1997 Proceedings},
  year = 1997,
  series = {Lecture Notes on Artificial Intelligence},
  volume = {1297},
  note = {The original publication is available at \url{http://www.springerlink.com}},
  publisher = {Springer Verlag},
  address = {Heidelberg, \Germany},
  month = sep,
  scopus = {2-s2.0-84957894431},
  keywords = {Abduction, Negation, Integrity_Constraints},
  isbn = {3-540-63514-9},
  issn = {0302-9743},
  doi = {10.1007/3540635149_47},
  pages = {181 -- 188},
  http = {http://link.springer.com/chapter/10.1007%2F3540635149_47},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/KakRigu-ILP97.pdf},
  copyright = {Springer},
  keywords = {Machine Learning, Abduction, Logic Programming, Inductive Logic Programming}
}
@inproceedings{LamMelMil97-AI*IA97-IC,
  author = {Evelina ~Lamma AND Paola ~Mello AND Michela ~Milano AND Fabrizio
   ~Riguzzi},
  title = {Introducing Abduction into (Extensional)
        Inductive Logic Programming Systems},
  booktitle = {{AI*IA} 97: Advances in Artificial Intelligence: 5th Congress of the Italian Association for Artificial Intelligence Rome, Italy, September 17-19, Proceedings},
  editor = {M. Lenzerini},
  series = {Lecture Notes on Artificial Intelligence},
  volume = {1321},
  note = {The original publication is available at \url{http://www.springerlink.com}},
  publisher = {Springer Verlag},
  year = 1997,
  address = {Heidelberg, \Germany},
  keywords = {Abduction, Negation, Integrity_Constraints},
  month = sep,
  doi = {10.1007/3-540-63576-9_107},
  issn = {0302-9743},
  isbn = {3-540-63576-9},
  pages = {183 -- 194},
  http = {http://link.springer.com/chapter/10.1007/3-540-63576-9_107},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/LamMelMil-AIIA97.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 dened as an extension of the Inductive Logic Programming framework.
         In the new problem denition, both the background and the target theories 
         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-s2.0-84961356665},
  keywords = {Machine learning, Nonmonotonic reasoning}
}
@inproceedings{RIG97-COMPULOG97-IW,
  author = {Fabrizio Riguzzi},
  title = {Using Abductive Logic Programming as a Representation Formalism for {ILP}},
  booktitle = {Proceedings of the CompulogNet Area Meeting ``Computational
    Logic and Machine Learning: Representation Issues in Reasoning and Learning''},
  year = 1997,
  month = sep,
  keywords = {Abduction, Knowledge Representation, Inductive Logic Programming},
  address = {Prague, \CzechRepublic},
  pages = {15--18},
  publisher = {Compulog Net},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/Rig-COMPULOG97.pdf},
  abstract = {In this paper we summarize the work done by the nodes of Bologna and Cyprus
on the use of Abductive Logic Programming (ALP) as a representation
formalism for Inductive Logic Programming. In this case, both the
background knowledge and the target program are abductive logic programs
and the coverage of examples through the resolution proof procedure of
Prolog is replaced by coverage through an abductive proof procedure.
}
}
@inproceedings{LamMelMil97-LPKR97-IW,
  author = {Evelina ~Lamma AND Paola ~Mello AND Michela ~Milano AND Fabrizio
   ~Riguzzi},
  title = {A System for Learning Abductive Logic Programs},
  booktitle = {Proceedings of the {ILPS97} Workshop on Logic Programming and
Knowledge Representation (LPKR97), Port Jefferson, New York, USA,  October 17, 1997},
  editor = {J. Dix and L. M. Pereira and T. Przymusinski},
  year = {1997},
  publisher = {Universit\"at Koblenz\--Landau, Institut f\"ur Informatik},
  keywords = {Abduction, Negation, Integrity_Constraints},
  month = oct,
  address = {Koblenz, \Germany},
  pages = {55--66},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/LamMelMil-LPKR97-IW.pdf},
  abstract = {We present the system LAP for  learning abductive logic programs
from examples and from a background abductive theory.
A new type of induction problem has been defined as an extension of
the Inductive Logic Programming framework. In the new problem definition,
both the background and the
target theories are abductive logic programs and the coverage of examples
is
replaced by abductive coverage.
LAP is based on a top-down learning algorithm that has been suitably
extended in order to solve the new induction problem. In particular,
the testing of example coverage is performed by using the abductive proof
procedure
defined by Kakas and Mancarella.
Assumptions can be made in order to cover positive examples and rule out
negative ones and these assumptions can be used as new training data.
LAP can be applied for learning in presence of incomplete knowledge
and for learning exceptions to classification rules.}
}
@inproceedings{MilOmiRig97-NW,
  author = {Michela Milano and Andrea Omicini and Fabrizio Riguzzi},
  title = {Learning with an Object-Oriented Data Model},
  booktitle = {Incontro dei Gruppi di Lavoro su Apprendimento Automatico e
    Linguaggio Naturale dell'Associazione
    Italiana per l'Intelligenza Artificiale (AIIA)},
  month = dec,
  year = 1997,
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/MilOmiRig-AALN97.pdf}
}

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