Fuzzy Logic and the Resolution Principle
Journal of the ACM (JACM)
Semiring-based constraint logic programming: syntax and semantics
ACM Transactions on Programming Languages and Systems (TOPLAS)
A Procedural Semantics for Multi-adjoint Logic Programming
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Fuzzy Prolog: A Simple General Implementation Using CLP(R)
LPAR '02 Proceedings of the 9th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning
Multi-adjoint Logic Programming with Continuous Semantics
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Logic programs with uncertainties: a tool for implementing rule-based systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Building a fuzzy transformation system
SOFSEM'06 Proceedings of the 32nd conference on Current Trends in Theory and Practice of Computer Science
Fuzzy Logic, Soft Computing, and Applications
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Systemic approach to fuzzy logic formalization for approximate reasoning
Information Sciences: an International Journal
Information Sciences: an International Journal
| Hi-index | 0.00 |
Fuzzy reasoning is a very productive research field that during the last years has provided a number of theoretical approaches and practical implementation prototypes. Nevertheless, the classical implementations, like Fril, are not adapted to the latest formal approaches, like multi-adjoint logic semantics. Some promising implementations, like Fuzzy Prolog, are so general that the regular user/programmer does not feel comfortable because either the representation of fuzzy concepts is complex or the results of the fuzzy queries are difficult to interpret. In this paper we present a modern framework, RFuzzy , that is modeling multi-adjoint logic in a practical way. It provides some extensions as default values (to represent missing information), partial default values (for a subset of data) and typed variables. RFuzzy represents the truth value of predicates using facts, rules and also can define fuzzy predicates as continuous functions. Queries are answered with direct results (instead of providing complex constraints), so it is easy to use for any person that wants to represent a problem using fuzzy reasoning in a simple way (just using the classical fuzzy representation with real numbers). The most promising characteristic of RFuzzy is that the user can obtain constructive answers to queries that restrict the truth value.