Conference Program

 

---

Detailed Program

Wednesday 3rd October

08.30 – 09.15     Registration

09.15 – 09.30     Opening

09.30 – 10.30     Session 1

• Murat Diker, Ayşegül Altay Uğur and Sadık Bayhan, Textural Formal Concepts
• Murat Diker and Ayşegül Altay Uğur, Dicovering Approximation Spaces
• Pilar Pozos-Parra, Laurent Perrussel and Jean-Marc Thévenin, On Enumerating Models for the Logic of Paradox using Tableau

10.30 – 11.00     Coffee break

10.40 – 12.00     Session 2

• Olivier Pivert and Henri Prade, Handling Uncertainty in Relational Databases with Possibility Theory – A Survey of Different Modelings
• Christophe Marsala, Anne Laurent, Marie-Jeanne Lesot, Maria Rifqi, and Arnaud Castelltort, Discovering Ordinal Attributes through Gradual Patterns, Morphological Filters and Rank Discrimination Measures
• Matthias Nickles, Distribution-Aware Sampling of Answer Sets

12.00 – 13.00     Keynote talk: Georg Gottlob, Swift Logic for Big Data and Knowledge Graphs

13.00 – 14.00     Lunch

14.00 – 15.00     Session 3

• Abdelhak Imoussaten, An Approach Based on MCDA and Fuzzy Logic to Select Joint Actions
• Mengwei Xu, Kim Bauters, Kevin McAreavey and Weiru Liu, A Formal Approach to Embedding First-Principles Planning in BDI Agent Systems
• Arno Blaas, Adam Cobb, Jan Calliess and Stephen Roberts, Scalable Bounding of Predictive Uncertainty in Regression Problems with SLAC

15.00 – 15.45     Tutorial: Grégory Smits, Fuzzy querying: from theory to practice

15.45 – 16.15     Coffee break

16.15 – 17.00     Tutorial: Andrea Tettamanzi, Uncertainty in the Semantic Web: The case of axiom scoring

17.00 – 18.00     Session 4

• Ana Ozaki and Rafael Peñaloza, Consequence-based Axiom Pinpointing
• Giuseppe Cota, Fabrizio Riguzzi, Riccardo Zese, Elena Bellodi and Evelina Lamma A Modular Inference System for Probabilistic Description Logics
• Dario Malchiodi, Célia Da Costa Pereira and Andrea Tettamanzi, Predicting the Possibilistic Score of OWL Axioms through Support Vector Regression

Thursday, 4th October

09.00 – 10.00     Keynote talk: Dominik Slezak, Toward Approximate Intelligence: A Rough Set Perspective

10.00 – 10.30     Coffee break

10.30 – 11.50     Session 5

• Nico Potyka, Measuring Disagreement among Knowledge Bases
• Leopoldo Bertossi, Measuring and Computing Database Inconsistency via Repairs
• Zoltán Ernő Csajbók, Measuring Bipolarity in Pawlak Approximation Spaces
• Maurice van Keulen, Benjamin Kaminski, Christoph Matheja and Joost-Pieter Katoen, Rule-based Conditioning of Probabilistic Data

11.50 – 12.35     Tutorial: Francesco Masulli, Unsupervised Tracking  of Time-Evolving Data Streams

12.35 – 14.00     Lunch

14.00 – 15.00     Session 6

• Paolo Viappiani, Positional Scoring Rules with Uncertain Weights
• Federico Cabitza and Davide Ciucci, Fuzzification of Ordinal Classes.The Case of the HL7 Severity Grading
• Marcos Luiz de Paula Bueno, Arjen Hommersom, Peter Lucas, Mariana Lobo and Pedro Pereira Rodrigues, Modeling the dynamics of multiple disease occurrence by latent states

15.00 – 15.45     Tutorial: Agnes Rico, Discrete Sugeno integrals and their applications

15.45 – 16.15     Coffee break

16.15 – 17.00     Tutorial: Salvatore Greco, Multiple Criteria Decision Aiding:  basic ideas and current developments

17.00 – 18.00     Session 7

• Nahla Ben Amor, Didier Dubois, Henri Prade and Syrine Saidi, Representation of Multiple Agent Preferences – A Short Survey
• Maria Vanina Martinez, Lluis Godo and Gerardo I. Simari, Inferring Quantitative Preferences: Beyond Logical Deduction
• Didier Dubois, Francis Faux, Henri Prade and Agnes Rico Separable qualitative capacities

20.00     Social Dinner

Friday, 5th October

09.00 – 10.00     Keynote talk: Salem Benferhat, A practical handling of conflicting ontologies

10.00 – 10.30     Coffee break

10.30 – 11.50     Session 8

• Alessandro Antonucci and Facchini Alessandro, A Credal Extension of Independent Choice Logic
• Thomas Augustin, Imprecise Sampling Models for Modelling Unobserved Heterogeneity? Basic Ideas of a Credal Likelihood Concept
• Linda C. van der Gaag and Philippe Leray, Qualitative Probabilistic Relational Models
• Serena Doria, Integral representations of a coherent upper conditional prevision by the Symmetric Choquet integral and the Asymmetric Choquet integral with respect to Hausdorff outer measures

11.50 – 12.35     Tutorial: Didier Dubois, A crash course on generalized possibilistic logic

12.35 – 14.00     Lunch

14.00 – 15.00     Session 9

• Giuseppe Sanfilippo, Lower and Upper probability Bounds for some Conjunctions of Two Conditional Events
• Niki Pfeifer and Giuseppe Sanfilippo, Probabilistic semantics for categorical syllogisms of Figure II
• Doretta Vivona and Maria Divari, A new measure of general information on pseudo-analysis

15.00 – 15.45     Tutorial: Fabio Stella, Continuous Time Bayesian Networks

15.45 – 16.15     Coffee break

16.15 – 17.15     Session 10

• Antonio Rago, Pietro Baroni and Francesca Toni On Instantiating Generalised Properties of Gradual Argumentation Frameworks
• Juan Carlos L. Teze, Lluis Godo and Guillermo R. Simari, An argumentative recommendation approach based on contextual aspects
• Raphaela Butz, Arjen Hommersom and Marko van Eekelen, Explaining the most probable explanation

17.15 – 17.30     Closing

---

Tutorials

Hassan Ait-Kaci

Fuzzy Lattice Operations on First-Order Terms over Signatures with Similar Constructors— A Constraint-Based Approach

• Unification and generalization are operations on two terms computing respectively their greatest lower bound and least upper bound when the terms are quasi-ordered by subsumption up to variable renaming (i.e., t1 ≤ t2 iff t1 = t2σ for some variable substitution σ). When term signatures are such that distinct functor symbols may be related with a fuzzy equivalence (called a similarity), these operations can be formally extended to tolerate mismatches on functor names and/or arity or argument order. We reformulate and extend previous work with a declarative approach defining unification and generalization as sets of axioms and rules forming a complete constraint-normalization proof system. These include the Reynolds-Plotkin term-generalization procedures, Maria Sessa’s “weak” unification with partially fuzzy signatures and its corresponding generalization, as well as novel extensions of such operations to signatures with weaker functor similarities (i.e., with possibly different arities). One advantage of this approach is that it requires no modification of the conventional data structures for terms and substitutions. This and the fact that these declarative specifications are efficiently executable conditional Horn-clauses offers a great practical potential for fuzzy information-handling applications.
• Keywords: Approximate reasoning; Fuzzy inference systems; Fuzzy constraint satisfaction; Learning; Fuzzy databases; Information retrieval; Information lattices; First-order terms; Fuzzy unification; Fuzzy generalization.

---

Didier Dubois

A Crash Course on Generalized Possibilistic Logic

• This tutorial proposes a concise overview of the role of possibility theory in logical approaches to reasoning under uncertainty. It shows that three traditions of reasoning under or about uncertainty (set-functions, epistemic logic, and three-valued logics) can be reconciled in the setting of possibility theory. We offer a brief presentation of basic possibilistic logic, and of its generalization that comes close to a modal logic albeit with simpler more natural epistemic semantics. Past applications to various reasoning tasks are surveyed, and future lines of research are also outlined.

---

Salvatore Greco

Multiple Criteria Decision Aiding:  basic ideas and current developments

• Multiple criteria decision aiding gives a set of concepts, methods, techniques, and approaches to handle complex decision problems where a plurality of points of view has to be taken into account. After introducing the basic ideas and concepts, a survey on the main approaches and methodologies will be presented. Finally, some subjects that seem quite relevant for the future developments of the domain will be discussed: purely qualitative methodologies, interaction between criteria, hierarchy of criteria, robustness concerns, highly efficient preference-driven multiobjective optimization algorithms.

---

Francesco Masulli

Unsupervised Tracking  of Time-Evolving Data Streams

• Data streams have arisen as a relevant topic during the last decade. In this work we consider non-stationary data stream clustering using a possibilistic approach. The Graded Possibilistic Clustering model offers a way to evaluate “outlierness” through a natural measure, which is computed directly from the model. Both online and batch training approaches are considered, to provide different trade-offs between stability and speed of response to changes. The proposed approach is evaluated on a synthetic data set, for which the ground truth is available. Moreover, a real-time short-term urban traffic flow forecasting application is proposed, taking into account both spatial and temporal information. To this aim, we introduce a Layered Ensemble Model which combines Artificial Neural Networks and Graded Possibilistic Clustering models, obtaining in such a way an accurate forecaster of the traffic flow rates with outlier detection. Experimentation has been carried out on two different datasets. The former consists of real UK motorway data and the later is obtained from simulated traffic flow on a street network in Genoa (Italy). The proposed model for short-term traffic forecasting provides promising results and given its characteristics of outlier detection, accuracy, and robustness, and can be fruitfully integrated into traffic flow management systems.

---

Agnès Rico

Discrete Sugeno integrals and their applications

• This tutorial is an overview of the discrete Sugeno integrals and their applications when the evaluation scale is a totally ordered set. The various expressions of the Sugeno integrals are presented. Some major characterization results are recalled: results based on characteristicproperties and act-based axiomatization. We discuss the properties of a preference relation modeling by a Sugeno integral. We also present its power expression to represent a dataset and its interpretation with a set of if-then rules.

---

Grégory Smits

Fuzzy querying: from theory to practice

• The last decade has witnessed an increasing interest in expressing preferences inside database queries. As a matter of fact, the first research works on this topic date back to the late 80s. Approaches to database preference queries may be classified into two categories according to their qualitative or quantitative nature. Typical representatives of the first category are fuzzy set-based approaches which use membership functions that describe the preference profiles of the user on each attribute domain involved in the query. Despite numerous theoretical achievements during the last 30 years, fuzzy querying struggles to be considered as a mature solution to the enrichment of data access. In this tutorial, I will make a brief recall of the history of fuzzy querying to then enumerate the scientific, technological and psychological barriers to break down so as to convince end users about the usefulness of this preference query model.

---

Fabio Stella

Continuous Time Bayesian Networks

• Continuous time Bayesian networks combine Bayesian networks and homogeneous Markov processes together to efficiently model discrete state continuous time dynamical systems (Nodelman et al., 2002). They are particularly useful for modeling domains in which variables evolve at different time granularities, such as to model the presence of people at their computers (Nodelman & Horvitz, 2003), to study reliability of dynamical systems (Boudali & Dugan, 2006), to model failures in server farms (Herbrich, Graepel, & Murphy, 2007), to detect network intrusion (Xu & Shelton, 2008), to analyze social networks (Fan & Shelton, 2009), to model cardiogenic heart failure (Gatti, Luciani, & Stella, 2011) and to reconstruct gene regulatory networks (Acerbi & Stella, 2014; Acerbi, Vigano, Poidinger, Mortellaro, Zelante, & Stella, 2016). Recently, the complexity of inference in continuous time Bayesian networks has been studied (Sturlaugson & Sheppard, 2014).
  This tutorial is intended for researchers interested in learning about continuous-time Markov processes with specific reference to compact or structured representations of them through continuous time Bayesian networks. We first give basics of Bayesian networks, dynamic Bayesian networks, continuous time Markov processes, and main continuous time distributions. Then, the tutorial will introduce continuous time Bayesian networks together with their generative and amalgamation semantics. The tasks of inference and structural learning for continuous time Bayesian networks are introduced. Extension of continuous time Bayesian networks to take into account memory are presented by using the powerful class of phase-type distributions. The tutorial also presents some applications of continuous time Bayesian networks to medicine, biology, and finance.

---

Andrea Tettamanzi

Uncertainty in the Semantic Web: The case of axiom scoring

• Although no provision is made in the W3C standards to represent uncertainty in the Semantic Web, the need for handling it is inevitable, if one thinks of the open nature of the linked data, of the incompleteness and fallibility of the information sources, and of the open-world assumption underlying the Web ontology language (OWL).
  In this tutorial, the problem of scoring OWL axioms against RDF facts will provide an original angle of attack to approach the issue of uncertainty in the Semantic Web. After briefly discussing some problems a probabilistic approach runs into, I will present an alternative theoretical framework, based on possibility theory, for OWL axiom testing against the evidence provided by knowledge bases for which the open-world assumption holds. I will then illustrate its practical application to the test of SubClassOf axioms against the DBpedia RDF dataset.