@inproceedings{BelRigLam09-RICERCA-RCRA-IW,
author = {Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
title = {Mining Probabilistic Declarative Process Models},
booktitle = { Session {R.i.C.e.R.c.A}: RCRA Incontri E Confronti of the 16th RCRA International Workshop on Experimental evaluation of algorithms for solving
problems with combinatorial explosion ({RCRA} 2009)
Reggio Emilia, Italy, 11-12 December 2009},
editor = {Marco Gavanelli and Toni Mancini},
url = {http://ml.unife.it/wp-content/uploads/Papers/BelRigLam09-RICERCA-RCRA-IW.pdf},
year = {2009},
keywords = {Process Mining, Learning from Interpretations, Business Processes, Probabilistic Relational Languages},
abstract = {The management of business processes has recently received a lot of attention from companies, since it can support efficiency improvement. We present an approach for mining process models that first induces a model in the SCIFF logical language and then translates the model into Markov logic, a language belonging to the field of statistical relational learning.
Markov logic attaches weights to first-order contraints, in order to obtain a final probabilistic classification of process traces better than the purely logical one. The data used for learning and testing belong to a real database of university students' careers.}
}
@article{CheLamMel09-TOPNOC-IJ,
author = {Federico Chesani and Evelina Lamma and
Paola Mello and Marco Montali and Fabrizio Riguzzi and Sergio
Storari},
title = {Exploiting Inductive Logic Programming Techniques for Declarative
Process Mining},
journal = {LNCS Transactions on Petri Nets and Other Models of Concurrency,
{ToPNoC} {II}},
year = {2009},
publisher = {Springer},
address = {Heidelberg, \Germany},
note = {The original publication is available at \url{http://www.springerlink.com}},
series = {Lecture Notes on Computer Science},
volume = {5460},
pages = {278--295},
doi = {10.1007/978-3-642-00899-3_16},
issn = {1867-7193},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/CheLamMel-TOPNOC09.pdf},
url = {http://www.springerlink.com/content/c4j2k38675588759/},
abstract = {In the last few years, there has been a growing interest in the
adoption of declarative paradigms for modeling and verifying process
models. These paradigms provide an abstract and human understandable
way of specifying constraints that must hold among activities
executions rather than focusing on a specific procedural solution.
Mining such declarative descriptions is still an open challenge. In
this paper, we present a logic-based approach for tackling this
problem. It relies on Inductive Logic Programming techniques and,
in particular, on a modified version of the Inductive Constraint
Logic algorithm. We investigate how, by properly tuning the learning
algorithm, the approach can be adopted to mine models expressed in
the ConDec notation, a graphical language for the declarative
specification of business processes. Then, we sketch how such a
mining framework has been concretely implemented as a ProM plug-in
called DecMiner. We finally discuss the effectiveness of the
approach by means of an example which shows the ability of the
language to model concurrent activities and of DecMiner to learn
such a model.},
keywords = {Process Mining, Inductive Logic Programming, Declarative Process Languages},
copyright = {Springer}
}
@article{StoRigLam09-IDA-IJ,
author = {Sergio Storari and Fabrizio Riguzzi and Evelina Lamma},
title = {Exploiting Association and Correlation Rules Parameters for Learning
Bayesian Networks},
journal = {Intelligent Data Analysis},
year = {2009},
pages = { 689--701},
publisher = {{IOS} Press},
volume = {13},
issue = {5},
address = {Amsterdam, \TheNetherlands},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/StoRigLam-IDA09.pdf},
doi = {10.3233/IDA-2009-0388},
abstract = { In data mining, association and correlation rules
are inferred from data in order to highlight statistical dependencies among
attributes. The metrics defined for evaluating these rules can be exploited to
score relationships between attributes in Bayesian network learning. In this
paper, we propose two novel methods for learning Bayesian networks from data
that are
based on the K2 learning algorithm and that improve it by exploiting parameters
normally defined for association and correlation rules.
In particular, we propose the algorithms K2-Lift and K2-$X^{2}$, that exploit
the lift metric and the $X^2$ metric respectively. We compare
K2\--Lift, K2-$X^{2}$ with K2 on artificial data and on
three test Bayesian networks. The experiments show that both our algorithms
improve K2 with respect to the quality of the
learned network. Moreover, a comparison of K2\--Lift and K2-$X^{2}$ with a
genetic algorithm approach on two benchmark networks show superior results on
one network and comparable results on the other.},
keywords = {Bayesian Networks Learning, K2, Association Rules, Correlation
Rules},
copyright = {Sergio Storari, Fabrizio Riguzzi and Evelina Lamma, exclusively licensed to {IOS} Press}
}
@inproceedings{AlbGavLam09-CEUR-NW,
author = {Marco Alberti and
Marco Gavanelli and
Evelina Lamma and
Fabrizio Riguzzi and
Sergio Storari },
editor = {Matteo Baldoni and
Cristina Baroglio},
booktitle = {Il Milione (i.e. $2^6$, June 3rd 2008)
A Journey in the Computational Logic in Italy, Proceedings of the Day Dedicated to Prof. {Alberto Martelli}
Turin, Italy, June 3, 2008},
title = {Inducing Specification of Interaction Protocols and Business Processes and Proving their Properties},
year = {2009},
abstract = {In this paper, we overview our recent research
activity concerning the induction of Logic Programming
specifications, and the proof of their properties via Abductive
Logic Programming. Both the inductive and abductive tools here
briefly described have been applied to respectively learn and verify
(properties of) interaction protocols in multi-agent systems, Web
service choreographies, careflows and business processes.},
pdf = {http://ceur-ws.org/Vol-487/paper6.pdf},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
volume = {487},
pages = {32-37},
address = {Aachen, \Germany},
keywords = {Business Process Management, Logic Programming}
}
@incollection{TorCheMel09-OMAS-BC,
author = {Paolo Torroni and Federico Chesani and Paola Mello and Pinar Yolum and Munindar P. Singh and Marco Alberti and Marco Gavanelli and Evelina Lamma},
title = {Modeling Interactions via Commitments and Expectations},
year = {2009},
publisher = {Information Science Reference},
editor = {Virginia Dignum},
booktitle = {Handbook of Research on Multi-Agent Systems: Semantics and Dynamics of
Organizational Models},
month = {March},
pages = {263--284},
url = {http://www.igi-global.com/reference/details.asp?ID=33141},
abstract = {Organizational models often rely on two assumptions: openness and heterogeneity. This is, for instance,
the case with organizations consisting of individuals whose behaviour is unpredictable, whose internal
structure is unknown, and who do not necessarily share common goals, desires, or intentions. This
fact has motivated the adoption of social-based approaches to modelling interaction in organizational
models. The idea of social semantics is to abstract away from the agent internals and provide a social
meaning to agent message exchanges. In this chapter, we present and discuss two declarative, social
models interaction in terms of commitments. The second one adopts a rule-oriented perspective, and
models interaction in terms of logical formulae expressing expectations about agent interaction. We use
a simple interaction protocol taken from the e-commerce domain to present the functioning and features
of the commitment- and expectation-based approaches, and to discuss various forms of reasoning and
verification that they accommodate, and how organizational modelling can benefit from them},
isbn = { 978-1-60566-256-5},
keywords = {Social commitments, Social expectations, Interaction protocols, Open agent societies, Semantics of interaction}
}
@inproceedings{AlbCatGav09-ICWS-IC,
author = {Marco Alberti and Massimiliano Cattafi and Marco Gavanelli and Evelina Lamma and
Federico Chesani and Marco Montali and Paola Mello and Paolo Torroni},
title = {Integrating Abductive Logic Programming and Description Logics in a Dynamic Contracting Architecture},
year = 2009,
editor = {Paul Hofmann},
booktitle = {ICWS 2009: 2009 IEEE International Conference on Web Services},
accept_rate = {15.6\%},
abstract = {In Semantic Web technologies, searching for a service means to identify
components that can potentially satisfy the user
needs in terms of outputs and effects (discovery), and that, when invoked
by the customer, can fruitfully interact with her (contracting).
In this paper, we present an application framework that encompasses both the
discovery and the contracting steps, in a unified search process.
In particular, we accommodate service discovery by
ontology-based reasoning, and contracting by reasoning about policies published in a formal
language. To this purpose, we consider a formal approach grounded on Computational Logic,
and Abductive Logic Programming in particular. We propose a framework,
called SCIFF Reasoning Engine,
able to establish, by ontological and abductive reasoning, if a semantic web service and a requester can
fruitfully inter-operate, taking as input the behavioural interfaces
of both the participants, and producing as output a
sort of a contract.},
url = {http://conferences.computer.org/icws/2009/},
publisher = {IEEE Computer Society Press},
pages = {254--261},
doi = {10.1109/ICWS.2009.78},
organization = {IEEE Computer Society},
isbn = {978-0-7695-3709-2}
}
@inproceedings{GavAlbLam09-ICLP-IC,
author = {Marco Gavanelli and Marco Alberti and Evelina Lamma},
title = {Integration of abductive reasoning and constraint optimization in {SCIFF}},
year = 2009,
editor = {Patricia M. Hill and David S. Warren},
series = {Lecture Notes in Computer Science},
volume = 5649,
pages = {387--401},
booktitle = {25th International Conference on Logic Programming (ICLP 2009)},
abstract = {Abductive Logic Programming (ALP) and Constraint Logic Programming (CLP) share
the feature to constrain the set of possible solutions to a program via integrity or CLP constraints.
These two frameworks have been merged in works by various authors, which developed efficient abductive proof-procedures empowered with constraint satisfaction techniques.
However, while almost all CLP languages provide algorithms for finding an optimal solution with respect to some objective function (and not just {\em any} solution), the issue has received little attention in ALP.
In this paper we show how optimisation meta-predicates can be included in abductive proof-pro\-ce\-dures, achieving in this way a significant improvement to research and practical applications of abductive reasoning.
In the paper, we give the declarative and operational semantics of an abductive proof-procedure that encloses constraint optimization meta-predicates, and we prove soundness in the three-valued completion semantics.
In the proof-procedure, the abductive logic program can invoke optimisation meta-predicates, which can invoke abductive predicates, in a recursive way.},
keywords = {Abductive Logic Programming, Constraint Logic Programming, Constraint Optimization, Constraint Handling Rules},
url = {http://www.ist.unomaha.edu/iclp2009/},
publisher = {Springer-Verlag},
address = {Berlin Heidelberg},
issn = {0302-9743},
isbn = {978-3-642-02845-8}
}
@inproceedings{AlbCatGav09-CILC-NW,
author = {Marco Alberti and Massimiliano Cattafi and Marco Gavanelli and Evelina Lamma},
title = {Exploiting Semantic Technology in Computational Logic-based Service Contracting},
abstract = {Dynamic composition of web services requires an automated step of
contracting, i.e., the computation of a possibly fruitful
interaction between two (or more) services, based on their policies
and goals. In previous work, the SCIFF abductive logic language
was used to represent the services' policies, and the associated
proof procedure to perform the contracting.
In this paper, we build on that work in order to exploit the results
of the Description Logics research area to represent domain specific
knowledge, either by importing the knowledge encoded in an ontology
into a SCIFF knowledge base, or by interfacing the SCIFF proof
procedure to an existing ontological reasoner.},
booktitle = {CILC09: 24-esimo Convegno Italiano di Logica Computazionale},
editor = {Marco Gavanelli and Fabrizio Riguzzi},
year = {2009},
month = jun,
address = {Ferrara, Italy},
organization = {GULP},
pdf = {http://www.ing.unife.it/eventi/cilc09/papers/cilc09_submission_15.pdf}
}
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