A Complete Calculus for Possibilistic Logic Programming with Fuzzy Propositional Variables
This work addresses uncertainty representation in logic programming for domains like AI and decision-making, but it is incremental as it builds on existing possibilistic logic frameworks.
The paper tackles the problem of reasoning under possibilistic uncertainty and vague knowledge by developing a propositional logic programming language with fuzzy propositional variables, resulting in a complete calculus for determining maximum belief degrees and integrating fuzzy set matching.
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.