@article{LamPerRig00-ML-IJ, author = {Evelina Lamma and Fabrizio Riguzzi and Lu\'{i}s Moniz Pereira}, title = {Strategies in Combined Learning via Logic Programs}, journal = {Machine Learning}, volume = {38}, number = {1/2}, year = {2000}, month = {January/February}, pages = {63--87}, keywords = {ILP Implementation,ILP Theory,Knowledge Representation,Negation}, 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, ambiguous, or downright contradictory information. In a three-valued setting, we 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 (ELP) under a Well-Founded Semantics with explicit negation WFSX as the representation formalism for learning, and show how ELPs can be used to specify combinations of strategies in a declarative way also coping with contradiction and exceptions. Explicit negation is used to represent the opposite concept, while default negation is used to ensure consistency and to handle exceptions to general rules. Exceptions are represented by examples covered by the definition for a concept that belong to the training set for the opposite concept. Standard Inductive Logic Programming techniques are employed to learn the concept and its opposite. Depending on the adopted technique, we can learn the most general or the least general definition. Thus, four epistemological varieties occur, resulting from the combination of most general and least general solutions for the positive and negative concept. We discuss the factors that should be taken into account when choosing and strategically combining the generality levels for positive and negative concepts. In the paper, we also handle the issue of strategic combination of possibly contradictory learnt definitions of a predicate and its explicit negation. All in all, we show that extended logic programs under well-founded semantics with explicit negation add expressivity to learning tasks, and allow the tackling of a number of representation and strategic issues in a principled way. Our techniques have been implemented and examples run on a state-of-the-art logic programming system with tabling which implements WFSX.}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamRigPer-ML00.pdf}, publisher = {Springer Netherlands}, address = {Dordrecht, \TheNetherlands}, doi = {10.1023/A:1007681906490}, http = {http://link.springer.com/article/10.1023%2FA%3A1007681906490}, copyright = {Springer Netherlands}, note = {The original publication is available at \url{http://www.springerlink.com}} }

@incollection{EspFerLam00-BC, author = {Floriana Esposito AND Stefano Ferilli AND Evelina Lamma AND Paola Mello AND Michela Milano AND Fabrizio Riguzzi AND Giovanni Semeraro}, title = {Cooperation of Abduction and Induction in Logic Programming}, booktitle = {Abductive and Inductive Reasoning: Essays on thier Relation and Integration}, editor = {Peter A. Flach and Antonis C. Kakas}, publisher = {Kluwer Academic Publishers}, address = {Dordrecht, \TheNetherlands}, year = {2000}, abstract = {We propose an integration of abduction and induction where the two inference processes cooperate in order to perform more powerful inferences. We assume the definitions of abduction and induction as given in Abductive Logic Programming and Inductive Logic Programming. Abduction helps induction by generating atomic hypotheses that can be used as new examples or for completing an incomplete background knowledge. Induction helps abduction by generalizing explanations. We present a learning algorithm that integrates abduction and induction. The algorithm solves a new learning problem where both the background and the target theory are abductive theories and abductive derivability is used as the example coverage relation. We then show how the algorithm can be applied to learning from incomplete knowledge and learning exceptions.}, keywords = {Abduction, Negation, Integrity_Constraints}, month = apr, pages = {233--252}, http = {http://www.springer.com/gp/book/9780792362500}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamMelRig-ABDINDBK00.pdf}, copyright = {Kluwer Academic Publishers}, ebook-isbn = {978-94-017-0606-3}, doi = {10.1007/978-94-017-0606-3}, hardcover-isbn = {978-0-7923-6250-0}, softcover-isbn = {978-90-481-5433-3}, series = {Applied Logic Series}, issn = {1386-2790} }

@article{KakRig00-NGC-IJ, author = {Antonis C. Kakas AND Fabrizio Riguzzi}, title = {Abductive Concept Learning}, journal = {New Generation Computing}, volume = {18}, number = {3}, year = {2000}, pages = {243--294}, keywords = {Abduction, Integrity Constraints, Multiple Predicate Learning}, address = {Tokyo, \Japan}, month = may, publisher = {Ohmsha, Ltd. and Springer}, abstract = {We investigate how abduction and induction can be integrated into a common learning framework. In particular, we consider an extension of Inductive Logic Programming (ILP) for the case in which both the background and the target theories are abductive logic programs and where an abductive notion of entailment is used as the basic coverage relation for learning. This extended learning framework has been called Abductive Concept Learning (ACL). In this framework, it is possible to learn with incomplete background information about the training examples by exploiting the hypothetical reasoning of abduction. We also study how the ACL framework can be used as a basis for multiple predicate learning. An algorithm for ACL is developed by suitably extending the top-down ILP method: the deductive proof procedure of Logic Programming is replaced by an abductive proof procedure for Abductive Logic Programming. This algorithm also incorporates a phase for learning integrity constraints by suitably employing a system that learns from interpretations like ICL. The framework of ACL thus integrates the two ILP settings of explanatory (predictive) learning and confirmatory (descriptive) learning. The above algorithm has been implemented into a system also called ACL\footnote{The learning systems developed in this work together with sample experimental data can be found at the following address: {\tt http://www-lia.deis.unibo.it/Software/ACL/}} Several experiments have been performed that show the effectiveness of the ACL framework in learning from incomplete data and its appropriate use for multiple predicate learning.}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/KakRIg-NGC00.pdf}, http = {http://link.springer.com/article/10.1007%2FBF03037531}, doi = {10.1007/BF03037531}, copyright = {Ohmsha, Ltd. and Springer} }

@inproceedings{LamPerRig00-MSL00-IW, title = {Logic Aided {Lamarckian} Evolution}, author = {Evelina Lamma and Lu\'{i}s Moniz Pereira and Fabrizio Riguzzi}, booktitle = {Procs. of Multi-Strategy Learning Workshop (MSL00), Guimar\~{a}es, Portugal}, editor = {Pavel Brazdil and Ryszard S. Michalski}, publisher = {LIAAC - Universidade do Porto}, address = {Porto, \Portugal}, pages = {59--73}, month = jun, year = {2000}, keywords = {Genetic Algorithms,ILP Implementation,Theory Revision}, abstract = {We propose a multi-strategy genetic algorithm for performing belief revision. The algorithm implements a new evolutionary strategy which is a combination of the theories of Darwin and Lamarck. Therefore, the algorithm not only includes the Darwinian operators of selection, mutation and crossover but also a Lamarckian operator that changes the individuals so that they perform better in solving the given problem. This is achieved through belief revision directed mutations, oriented by tracing logical derivations. The algorithm, with and without the Lamarckian operator, is tested on a number of belief revision problems, and the results show that the addition of the Lamarckian operator improves the efficiency of the algorithm. We believe that the combination of Darwinian and Lamarckian operators will be useful not only for standard belief revision problems but especially for problems where the chromosomes may be exposed to different constraints and observations. In these cases, the Lamarckian and Darwinian operators would play a different role: the Lamarckian one would be used in order to bring a chromosome closer to a solution or to find an exact solution of the current belief revision problem, while Darwinian ones will have the aim of preparing chromosomes to deal with new situations by exchanging genes among them.}, url = {http://ml.unife.it/wp-content/uploads/Papers/LamPerRig-MSL00.pdf} }

@inproceedings{LamManMel00-IDAMAP00-IW, title = {A System for Monotoring Nosocomial Infections}, author = {Evelina Lamma and Marco Manservigi and Paola Mello and Fabrizio Riguzzi and Roberto Serra and Sergio Storari }, booktitle = {ECAI2000 Workshop on Intelligent Data Analysis in Medicine and Pharmacology ({IDAMAP}-2000), Berlin, 20-25 August 2000}, editor = {Nada Lavra\v{c} and Silvia Miksch and Branko Kav\v{s}ek }, publisher = {ECAI Workshop Notes}, month = aug, year = {2000}, address = {\Berlin, \Germany}, pages = {17--19}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamManMel-IDAMAP00.pdf} }

@inproceedings{CucMelPic00-MLCV00-IW, title = {An Application of Machine Learning and Statistics to Defect Detection}, author = { Rita Cucchiara and Paola Mello and Massimo Piccardi and Fabrizio Riguzzi}, booktitle = {ECAI2000 Workshop on Machine Learning in Computer Vision (MLCV00), Berlin, 22 August 2000}, editor = {Floriana Esposito and Donato Malerba}, publisher = {ECAI Workshop Notes}, month = aug, year = {2000}, address = {\Berlin, \Germany}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/CucMelPic-MLCV00.pdf} }

@inproceedings{LamManMel00-ISMDA00-IC, title = {A System for Monitoring Nosocomial Infections}, author = {Evelina Lamma and Marco Manservigi and Paola Mello and Roberto Serra and Sergio Storari and Fabrizio Riguzzi}, booktitle = {Medical Data Analysis: First International Symposium, {ISMDA} 2000, Frankfurt, Germany, September 29-30, 2000. Proceedings}, editor = {R. W. Brause and E. Hanisch }, publisher = {Springer Verlag}, abstract = {In this work, we describe a project, jointly started by DEIS University of Bologna and Dianoema S.p.A., in order to build a system which is able to monitor nosocomial infections. To this purpose, the system computes various statistics that are based on the count of patient infections over a period of time. The precise count of patient infections needs a precise definition of bacterial strains that is found by applying clustering to data on past infections. Moreover, the system is able to identify critical situations for a single patient (e.g., unexpected antibiotic resistance of a bacterium) or for hospital units (e.g., contagion events) and alarm the microbiologist.}, keywords = {Knowledge-based Systems; Nosocomial Infections}, pages = {282--292}, month = sep, year = {2000}, address = {Heidelberg, \Germany}, series = {{Lecture Notes on Computer Science}}, volume = {1933}, note = {The original publication is available at \url{http://www.springerlink.com}}, issn = {0302-9743}, doi = {10.1007/3-540-39949-6_34}, url = {http://www.springerlink.com/content/prqv16myudn9fbav/}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamManMel-ISMDA00.pdf}, copyright = {Springer}, scopus = {2-s2.0-33646094404}, wos = {WOS:000171225400034}, isbn = {3540410899} }

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