@incollection{CatLamRigSto10-ISKM-BC, author = {Massimiliano Cattafi and Evelina Lamma and Fabrizio Riguzzi and Sergio Storari}, title = {Incremental Declarative Process Mining}, booktitle = {Smart Information and Knowledge Management: Advances, Challenges, and Critical Issues}, year = {2010}, editor = {Ngoc Thanh Nguyen and Edward Szczerbicki}, publisher = {Springer}, address = {Heidelberg, \Germany}, series = {Studies in Computational Intelligence}, issn = {1860-949X}, isbn = {978-3-642-04583-7}, doi = {10.1007/978-3-642-04584-4_5}, volume = {260}, pages = {103--127}, abstract = {Business organizations achieve their mission by performing a number of processes. These span from simple sequences of actions to complex structured sets of activities with complex interrelation among them. The field of Business Processes Management studies how to describe, analyze, preserve and improve processes. In particular the subfield of Process Mining aims at inferring a model of the processes from logs (i.e. the collected records of performed activities). Moreover, processes can change over time to reflect mutated conditions, therefore it is often necessary to update the model. We call this activity Incremental Process Mining. To solve this problem, we modify the process mining system DPML to obtain IPM (Incremental Process Miner), which employs a subset of the SCIFF language to represent models and adopts techniques developed in Inductive Logic Programming to perform theory revision. The experimental results show that is more convenient to revise a theory rather than learning a new one from scratch. }, keywords = {Business Processes, Process Mining, Theory Revision}, url = {http://springerlink.com/content/663tx3001671j503/?p=1581a28611ac48088b750995a9767d5f&pi=4}, pdf = {http://ds.ing.unife.it/~friguzzi/Papers/CatLamRigSto09-ISKM-BC.pdf}, copyright = {Springer}, scopus = {2-s2.0-74049114164} }

@article{GavRigPet10-FI-EB, author = {Marco Gavanelli and Fabrizio Riguzzi and Alberto Pettorossi}, title = {Preface of the Special Issue on {CILC09}}, journal = {Fundamenta Informaticae}, volume = {105}, number = {1-2}, year = {2010}, ee = {http://dx.doi.org/10.3233/FI-2010-355}, bibsource = {DBLP, http://dblp.uni-trier.de}, http = {http://www.mimuw.edu.pl/~fundam/FI/previous/preface10512.pdf}, doi = {10.3233/FI-2010-355} }

@inproceedings{BelRigLam10-KSEM10-IC, author = {Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma}, title = {Probabilistic Declarative Process Mining}, booktitle = {Proceedings of the 4th International Conference on Knowledge Science, Engineering \& Management ({KSEM 2010}), Belfast, UK, September 1-3, 2010}, year = {2010}, editor = {Bi, Yaxin and Williams, Mary-Anne}, abstract = { The management of business processes is receiving much attention, since it can support signicant eciency improvements in organizations. One of the most interesting problems is the representation of process models in a language that allows to perform reasoning on it. Various knowledge-based languages have been lately developed for such a task and showed to have a high potential due to the advantages of these languages with respect to traditional graph-based notations. In this work we present an approach for the automatic discovery of knolwedge- based process models expressed by means of a probabilistic logic, starting from a set of process execution traces. The approach first uses the DPML (Declarative Process Model Learner) algorithm to extract a set of integrity constraints from a collection of traces. Then, the learned constraints are translated into Markov Logic formulas and the weights of each formula are tuned using the Alchemy system. The resulting theory allows to perform probabilistic classication of traces. We tested the proposed approach on a real database of university students' careers. The experiments show that the combination of DPML and Alchemy achieves better results than DPML alone.}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, address = {Heidelberg, \Germany}, volume = {6291}, pages = {292--303}, doi = {10.1007/978-3-642-15280-1_28}, pdf = {http://www.springerlink.com/content/h85k601v74850h5p/}, url = {http://ds.ing.unife.it/~friguzzi/Papers/BelRIgLam-KSEM10.pdf}, copyright = {Springer}, note = {The original publication is available at \url{http://www.springerlink.com}} }

@inproceedings{RigSwi10-CILC10-NC, author = {Fabrizio Riguzzi and Terrance Swift}, title = {An Extended Semantics for Logic Programs with Annotated Disjunctions and its Efficient Implementation}, booktitle = {Proceedings of the 25th Italian Conference on Computational Logic ({CILC2010}), Rende, Italy, July 7-9, 2010.}, year = {2010}, abstract = { Logic Programming with Annotated Disjunctions (LPADs) is a formalism for modeling probabilistic information that has recently received increased attention. The LPAD semantics, while being simple and clear, suffers from the requirement of having function free-programs, which is a strong limitation. In this paper we present an extension of the semantics that removes this restriction and allows us to write programs modeling infinite domains, such as Hidden Markov Models. We show that the semantics is well-defined for a large class of programs. Moreover, we present the algorithm ``Probabilistic Inference with Tabling and Answer subsumption'' (PITA) for computing the probability of queries to programs according to the extended semantics. Tabling and answer subsumption not only ensure the correctness of the algorithm with respect to the semantics but also make it very efficient on programs without function symbols. PITA has been implemented in XSB and tested on six domains: two with function symbols and four without. The execution times are compared with those of ProbLog, cplint and CVE. PITA was almost always able to solve larger problems in a shorter time on both type of domains.}, series = {CEUR Workshop Proceedings}, publisher = {Sun {SITE} Central Europe}, issn = {1613-0073}, volume = {598}, address = {Aachen, \Germany}, pdf = {http://ceur-ws.org/Vol-598/paper16.pdf}, copyright = {by the authors} }

@inproceedings{BelRigLam10-CILC10-NC, author = {Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma}, title = {Probabilistic Logic-based Process Mining}, booktitle = {Proceedings of the 25th Italian Conference on Computational Logic ({CILC2010}), Rende, Italy, July 7-9, 2010.}, year = {2010}, abstract = { The management of business processes has recently received much attention, since it can support significant efficiency improvements in organizations. One of the most interesting problems is the description of a process model in a language, also equipped with an operational support, that allows checking the compliance of a process execution (trace) to the model. Another problem of interest is the induction of these models from data. In this paper, we present a logic-based approach for the induction of process models that are expressed by means of a probabilistic logic. The approach first uses the DPML algorithm to extract a set of integrity constraints from a collection of traces. Then, the learned constraints are translated into Markov Logic formulas and the weights for each formula are tuned using the Alchemy system. The resulting theory allows to perform probabilistic classification of traces. We tested the proposed approach on a real database of university students' careers. The experiments show that the combination of DPML and Alchemy achieves better results than DPML alone.}, series = {CEUR Workshop Proceedings}, publisher = {Sun {SITE} Central Europe}, issn = {1613-0073}, address = {Aachen, \Germany}, volume = {598}, pdf = {http://ceur-ws.org/Vol-598/paper17.pdf}, url = {http://ds.ing.unife.it/~friguzzi/Papers/BelRigLam-CILC10.pdf}, copyright = {by the authors} }

@techreport{RigDiM10-TR, author = {Fabrizio Riguzzi and Nicola {Di Mauro}}, title = {Application of the Information Bottleneck to {LPAD} Learning}, year = {2010}, institution = {Dipartimento di Ingegneria, Universit\`a di Ferrara, Italy}, number = {CS-2010-01}, url = {http://ds.ing.unife.it/~friguzzi/Papers/CS-2010-01.pdf} }

@inproceedings{RigSwi10-RCRA10-IW, author = {Fabrizio Riguzzi and Terrance Swift}, title = {Tabling and Answer Subsumption for Reasoning on Logic Programs with Annotated Disjunctions}, editor = {Marco Gavanelli and Toni Mancini}, booktitle = {Proceedings of the 17th {RCRA} International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, Bologna, Italy, June 10-11, 2010}, year = {2010}, abstract = {The paper presents the algorithm ``Probabilistic Inference with Tabling and Answer subsumption'' (PITA) for computing the probability of queries from Logic Programs with Annotated Disjunctions. PITA is based on a program transformation techniques that adds an extra argument to every atom. PITA uses tabling for saving intermediate results and answer subsumption for combining different answers for the same subgoal. PITA has been implemented in XSB and compared with the ProbLog, cplint and CVE systems. The results show that in almost all cases, PITA is able to solve larger problems and is faster than competing algorithms.}, keywords = {Probabilistic Logic Programming, Tabling, Answer Subsumption, Logic Programs with Annotated Disjunction, Program Transformation}, url = {http://ceur-ws.org/Vol-616/}, pdf = {http://ds.ing.unife.it/~friguzzi/Papers/RigSwi-RCRA10.pdf}, series = {CEUR Workshop Proceedings}, publisher = {Sun {SITE} Central Europe}, issn = {1613-0073}, volume = {616}, pages = {1-10}, address = {Aachen, \Germany}, scopus = {2-s2.0-84893638592} }

@inproceedings{RigSwi10-ICLP10-IC, author = {Fabrizio Riguzzi and Terrance Swift}, title = {{T}abling and {A}nswer {S}ubsumption for {R}easoning on {L}ogic {P}rograms with {A}nnotated {D}isjunctions}, booktitle = {Technical Communications of the 26th Int'l. Conference on Logic Programming (ICLP'10)}, volume = {7}, year = {2010}, editor = {M.~Hermenegildo and T.~Schaub}, month = jul, publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, address = {Dagstuhl, Germany}, isbn = {978-3-939897-17-0}, issn = {1868-8969}, license = {Creative Commons Attribution-Noncommercial-No Derivative Works 3.0}, abstract = {The paper presents the algorithm ``Probabilistic Inference with Tabling and Answer subsumption'' (PITA) for computing the probability of queries from Logic Programs with Annotated Disjunctions. PITA is based on a program transformation techniques that adds an extra argument to every atom. PITA uses tabling for saving intermediate results and answer subsumption for combining different answers for the same subgoal. PITA has been implemented in XSB and compared with the ProbLog, cplint and CVE systems. The results show that in almost all cases, PITA is able to solve larger problems and is faster than competing algorithms.}, keywords = {Probabilistic Logic Programming, Tabling, Answer Subsumption, Logic Programs with Annotated Disjunction, Program Transformation}, pages = {162--171}, url = {http://drops.dagstuhl.de/opus/volltexte/2010/2594/}, doi = {10.4230/LIPIcs.ICLP.2010.162} }

@article{GavRigMilCag10-ICLP10-IJ, author = {Marco Gavanelli and Fabrizio Riguzzi and Michela Milano and Paolo Cagnoli}, title = {{L}ogic-{B}ased {D}ecision {S}upport for {S}trategic {E}nvironmental {A}ssessment}, year = {2010}, editor = {M.~Hermenegildo and T.~Schaub}, month = jul, journal = {Theory and Practice of Logic Programming, 26th Int'l. Conference on Logic Programming (ICLP'10) Special Issue}, pdf = {http://ds.ing.unife.it/~friguzzi/Papers/GavRigMilCag-ICLP10.pdf}, url = {http://journals.cambridge.org/action/displayIssue?jid=TLP&volumeId=10&seriesId=0&issueId=4-6}, http = {http://journals.cambridge.org/repo_A78jDFM5}, volume = {10}, number = {4-6}, publisher = {Cambridge University Press}, copyright = {Cambridge University Press}, abstract = {Strategic Environmental Assessment is a procedure aimed at introducing systematic assessment of the environmental effects of plans and programs. This procedure is based on the so called coaxial matrices that define dependencies between plan activities (infrastructures, plants, resource extractions, buildings, etc.) and positive and negative environmental impacts, and dependencies between these impacts and environmental receptors. Up to now, this procedure is manually implemented by environmental experts for checking the environmental effects of a given plan or program, but it is never applied during the plan/program construction. A decision support system, based on a clear logic semantics, would be an invaluable tool not only in assessing a single, already defined plan, but also during the planning process in order to produce an optimized, environmentally assessed plan and to study possible alternative scenarios. We propose two logic-based approaches to the problem, one based on Constraint Logic Programming and one on Probabilistic Logic Programming that could be, in the future, conveniently merged to exploit the advantages of both. We test the proposed approaches on a real energy plan and we discuss their limitations and advantages.}, keywords = {Strategic Environmental Assessment, Regional Planning, Constraint Logic Programming, Probabilistic Logic Programming, Causality}, doi = {10.1017/S1471068410000335}, pages = {643--658}, arxiv = {1007.3159} }

@article{Rig10-FI-IJ, author = {Fabrizio Riguzzi}, title = {{SLGAD} Resolution for Inference on {Logic Programs with Annotated Disjunctions}}, journal = {Fundamenta Informaticae}, abstract = {Logic Programs with Annotated Disjunctions (LPADs) allow to express probabilistic information in logic programming. The semantics of an LPAD is given in terms of well\--founded models of the normal logic programs obtained by selecting one disjunct from each ground LPAD clause. Inference on LPADs can be performed using either the system Ailog2, that was developed for the Independent Choice Logic, or SLDNFAD, an algorithm based on SLDNF. However, both of these algorithms run the risk of going into infinite loops and of performing redundant computations. In order to avoid these problems, we present SLGAD resolution that computes the (conditional) probability of a ground query from a range\--restricted LPAD and is based on SLG resolution for normal logic programs. As SLG, it uses tabling to avoid some infinite loops and to avoid redundant computations. The performances of SLGAD are evaluated on classical benchmarks for normal logic programs under the well\--founded semantics, namely a 2\--person game and the ancestor relation, and on a game of dice. SLGAD is compared with Ailog2 and SLDNFAD on the problems in which they do not go into infinite loops, namely those that are described by a modularly acyclic program. On the 2\--person game and the ancestor relation, SLGAD is more expensive than SLDNFAD on problems where SLDNFAD succeeds but is faster than Ailog2 when the query is true in an exponential number of instances. If the program requires the repeated computation of similar goals, as for the dice game, then SLGAD outperforms both Ailog2 and SLDNFAD.}, keywords = {Probabilistic Logic Programming, Well-Founded Semantics, Logic Programs with Annotated Disjunctions, SLG Resolution}, month = oct, volume = {102}, number = {3-4}, year = {2010}, pages = {429--466}, doi = {10.3233/FI-2010-392}, publisher = {{IOS} Press}, pdf = {http://ds.ing.unife.it/~friguzzi/Papers/Rig10-FI-IJ.pdf}, url = {http://iospress.metapress.com/content/h284771xj3rv48v1/?p=5a13bfb0811a4f8d88d661eb7cf8a649&pi=7}, scopus = {2-s2.0-78650327867}, isi = {WOS:000284311600008} }

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