Teresa Alsinet

EUMAS Conference 2005 Conference Paper

Expansion Operators for Modelling Agent Reasoning in Possibilistic Defeasible Logic Programming

• Carlos Iván Chesñevar
• Guillermo Ricardo Simari
• Lluís Godo
• Teresa Alsinet

SAT Conference 2005 Conference Paper

Improved Exact Solvers for Weighted Max-SAT

• Teresa Alsinet
• Felip Manyà
• Jordi Planes

Abstract We present two new branch and bound weighted Max-SAT solvers ( Lazy and Lazy *) which incorporate original data structures and inference rules, and a lower bound of better quality.

UAI Conference 2004 Conference Paper

A Logic Programming Framework for Possibilistic Argumentation with Vague Knowledge

• Carlos Iván Chesñevar
• Guillermo Ricardo Simari
• Teresa Alsinet
• Lluís Godo

Defeasible argumentation frameworks have evolved to become a sound setting to formalize commonsense, qualitative reasoning from incomplete and potentially inconsistent knowledge. Defeasible Logic Programming (DeLP) is a defeasible argumentation formalism based on an extension of logic programming. Although DeLP has been successfully integrated in a number of different real-world applications, DeLP cannot deal with explicit uncertainty, nor with vague knowledge, as defeasibility is directly encoded in the object language. This paper introduces P-DeLP, a new logic programming language that extends original DeLP capabilities for qualitative reasoning by incorporating the treatment of possibilistic uncertainty and fuzzy knowledge. Such features will be formalized on the basis of PGL, a possibilistic logic based on Godel fuzzy logic.

UAI Conference 2000 Conference Paper

A Complete Calcultis for Possibilistic Logic Programming with Fuzzy Propositional Variables

• Teresa Alsinet
• Lluís Godo

In this paper we present a propositional logic programming language for reasoning under possibilistic uncertainty and representing vague knowledge. Formulas are represented by pairs (A, c), where A is a many-valued proposition and c is value in the unit interval [0,1] which denotes a lower bound on the belief on A in terms of necessity measures. Belief states are modeled by possibility distributions on the set of all many-valued interpretations. In this framework, (i) we define a syntax and a semantics of the general underlying uncertainty logic; (ii) we provide a modus ponens-style calculus for a sublanguage of Horn-rules and we prove that it is complete for determining the maximum degree of possibilistic belief with which a fuzzy propositional variable can be entailed from a set of formulas; and finally, (iii) we show how the computation of a partial matching between fuzzy propositional variables, in terms of necessity measures for fuzzy sets, can be included in our logic programming system.

UAI Conference 1999 Conference Paper

On the Semantics and Automated Deduction for PLFC, a Logic of Possibilistic Uncertainty and Fuzziness

• Teresa Alsinet
• Lluís Godo
• Sandra A. Sandri

Possibilistic logic is a well-known graded logic of uncertainty suitable to reason under incomplete information and partially inconsistent knowledge, which is built upon classical first order logic. There exists for Possibilistic logic a proof procedure based on a refutation complete resolution-style calculus. Recently, a syntactical extension of first order Possibilistic logic (called PLFC) dealing with fuzzy constants and fuzzily restricted quantifiers has been proposed. Our aim is to present steps towards both the formalization of PLFC itself and an automated deduction system for it by (i) providing a formal semantics; (ii) defining a sound resolution-style calculus by refutation; and (iii) describing a first-order proof procedure for PLFC clauses based on (ii) and on a novel notion of most general substitution of two literals in a resolution step. In contrast to standard Possibilistic logic semantics, truth-evaluation of formulas with fuzzy constants are many-valued instead of boolean, and consequently an extended notion of possibilistic uncertainty is also needed.