Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Fuzzy Logic and the Resolution Principle
Journal of the ACM (JACM)
Fundamenta Informaticae
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
A Complete Calcultis for Possibilistic Logic Programming with Fuzzy Propositional Variables
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Possibilistic temporal reasoning based on fuzzy temporal constraints
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Representing Uncertainty in RuleML
Fundamenta Informaticae
Hi-index | 0.00 |
In a recent work we defined a possibilistic logic programming language, called PGL+, dealing with fuzzy propositions and with a fuzzy unification mechanism. The proof system, modus ponens-style, was shown to be complete when restricted to a class of Horn clauses satisfying two types of constraints. In this paper we complete the definition of the logic programming system. In particular, we first formalize a notion of PGL+ program and discuss the two types of constraints (called modularity and context constraints) we argue they must satisfy; second, we extend the PGL+ calculus with a chaining and fusion mechanism whose application ensures the fulfillment of the modularity constraint; and finally, we define an efficient (as much as possible) proof procedure oriented to goals which is complete for PGL+ programs satisfying the context constraint.