Reasoning with Preferences, Uncertainty and Vagueness
To be held on July 19th, 2018 as part of FLOC 2018
Submission: April 30, 2018 (extended!)
Notification: May 19, 2018
Camera Ready: May 27, 2018 (strict!)
Workshop: July 19, 2018
Call for Papers
Aims and Scope
PRUV 2018 is the Second Workshop on Logics for Reasoning about Preferences, Uncertainty, and Vagueness. This workshop follows a successful first edition (PRUV 2014), which culminated in a Special Issue of the IfCoLog Journal of Logics and their Applications.
Originally, managing preferences, uncertainty, and vagueness has in particular been explored in Artificial Intelligence. During the recent years, especially with the availability of massive amounts of data in different repositories and the possibility of integrating and exploiting these data, technologies for managing preferences, uncertainty, and vagueness have started to play a key role in other areas, such as databases and the (Social or Semantic) Web. These application areas have sparked another wave of strong interest into logics for dealing with preferences, uncertainty, and vagueness. Important examples are fuzzy and probabilistic approaches for description logics or rule systems for handling vagueness and uncertainty in the Semantic Web, or formalisms for handling user preferences in the context of ontological knowledge in the Social Semantic Web.
The aim of PRUV is to bring together people from different communities (such as the Artificial Intelligence and the Semantic Web community, among others), including theorists and practitioners, working on logics for reasoning about preferences, uncertainty, and vagueness.
Making researchers aware of and fruitfully discuss the most recent application areas, new challenges and the existing body of work on logics for reasoning about preferences, uncertainty, and vagueness, respectively is the main goal of this meeting.
PRUV welcomes submissions relating logic with preferences, uncertainty and vagueness. Some logics of interest are:
- first order logic,
- propositional logic,
- logic programming,
- answer set programming,
- description logics,
- modal logic,
- dynamic logic,
- temporal logics,
- agent logics.
Formalisms for handling preferences, uncertainty and vagueness include, but are not limited to
- probability measures,
- Bayesian networks,
- possibility measures,
- preference networks,
- rough sets,
- fuzzy set theory,
- similarity measures.
All accepted papers will be made available electronically at the CEUR Workshop Proceedings website (http://www.CEUR-ws.org/).
Double Submission Policy
The aim of the workshop is to bring together experts from a wide spectrum of research areas. Thus, we accept submissions of papers and results previously published in other major conferences and journals.
All submissions must be prepared in Springer’s LaTeX style llncs (http://www.springer.com/comp/lncs/Authors.html).
There are three submission formats:
- Full papers (up to 15 pages)
- Technical Communications (up to 8 pages)
- System Descriptions (up to 8 pages)
Submissions must be made via EasyChair at https://easychair.org/conferences/?conf=pruv18
Thomas Lukasiewicz – University of Oxford, United Kingdom
Rafael Peñaloza – Free University of Bozen-Bolzano, Italy
Anni-Yasmin Turhan – Technische Universität Dresden, Germany
Giovanni Amendola – University of Calabria, Italy
Eva Armengol – IIIA-SIC, Spain
Vaishak Belle – University of Edinburgh, United Kingdom
Fernando Bobillo – University of Zaragoza, Spain
Fabio Cozman – University of São Paulo, Brazil
Tommaso Di Noia – Politecnico di Bari, Italy
Andreas Ecke – TU Dresden, Germany
Pietro Galliani – Free University of Bozen-Bolzano, Italiy
Angelika Kimmig – Cardiff University, United Kingdom
Maria Vanina Martinez – Universidad Nacional del Sur in Bahia Blanca, Argentina
Nico Potyka – Universitaet Osnabrueck, Germany
Steven Schockaert – Cardiff University, United Kingdom
Gerardo Simari – Universidad Nacional del Sur, Argentina
Ivan Varzinczak – Univ. Artois & CNRS, France
Title: Quantitative Logic Reasoning – Combining logical reasoning with probabilities and counting
Speaker: Marcelo Finger, University of Sao Paulo, Brazil
Abstract: We present a research program which investigates the intersection of deductive reasoning with explicit quantitative capabilities. These capabilities encompass probabilistic reasoning, counting and counting quantifiers, and similar systems. The need to have a combined reasoning system that enables a unified way of reasoning with quantities has always been recognized in modern logic, as proposals of probabilistic logic reasoning are present since the work of Boole . Equally ubiquitous is the need to deal with cardinality restrictions on finite sets. More recently, a well-founded probabilistic theory has been developed for non-classical settings as well, such as probabilistic reasoning over Lukasiewicz infinitely-valued logic.
We show that there is a common way to deal with these several deductive quantitative capabilities, involving a framework based on Linear Algebra and Linear Programming. The distinction between classical and non-classical reasoning on the one hand, and probabilistic and cardinality reasoning on the other hand, comes from the different family of algebras employed. The quantitative logic systems also allow for the introduction of inconsistency measurements, which quantify the degree of inconsistency of a given quantitative logic theory, following some basic principles of inconsistency measurements.
On the practical level, we aim at exploring quantitative logic systems in which the complexity of reasoning is “only NP-complete”. We provide open-source implementations for solvers operating over those systems and study some notable empirical properties, such as the present of a phase transition.
Title: An abstraction-refinement framework for automated reasoning – a pragmatic approach
Speaker: Konstantin Korovin, University of Manchester, UK
Abstract: Abstraction-refinement is widely used in verification but, with some exceptions, is largely overlooked in the state-of-the-art automated theorem proving. One of our main motivations comes from the problem of reasoning with large theories where usually only a small part of the theory is needed for proving the conjecture. Efficient reasoning with large theories is one of the main challenges in automated theorem proving arising in many applications including reasoning with ontologies, large mathematical theories and verification. Current methods for reasoning with large theories are mainly based on axiom selection which we believe is limited in the scope. We propose a general approach to automated reasoning based on abstraction-refinement which aims at over and under approximation of theories in order to speed up reasoning. In particular, in this approach axiom selection can be seen as a special case of under approximation. In this talk we present a general abstraction-refinement framework for automated reasoning, draw connections with current approaches and discuss some practical abstractions. This is a joint work with Julio Cesar Lopez Hernandez.