Editorial: fuzzy set and possibility theory-based methods in artificial intelligence
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Geometrical Representation of Quantity Space and Its Application to Robot Motion Description
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Incremental controlled relaxation of failing flexible queries
Journal of Intelligent Information Systems
Answer Sets in a Fuzzy Equilibrium Logic
RR '09 Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems
Making fuzzy absolute and fuzzy relative orders of magnitude consistent
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Improving Decision Quality Through Preference Relaxation
Proceedings of the 2010 conference on Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the Next Decade
Disclosing false identity through hybrid link analysis
Artificial Intelligence and Law
OTM'05 Proceedings of the 2005 Confederated international conference on On the Move to Meaningful Internet Systems - Volume >Part I
Relaxation paradigm in a flexible querying context
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
| Hi-index | 0.00 |
This paper proposes a fuzzy set-based approach for handling relative orders of magnitude stated in terms of closeness and negligibility relations. At the semantic level, these relations are represented by means of fuzzy relations controlled by tolerance parameters. A set of sound inference rules, involving the tolerance parameters, is provided, in full accordance with the combination/projection principle underlying the approximate reasoning method of Zadeh. These rules ensure a local propagation of fuzzy closeness and negligibility relations. A numerical semantics is then attached to the symbolic computation process. Required properties of the tolerance parameter are investigated, in order to preserve the validity of the produced conclusions. The effect of the chaining of rules in the inference process can be controlled through the gradual deterioration of closeness and negligibility relations involved in the produced conclusions. Finally, qualitative reasoning based on fuzzy closeness and negligibility relations is used for simplifying equations and solving them in an approximate way, as often done by engineers who reason about a mathematical model. The problem of handling qualitative probabilities in reasoning under uncertainty is also investigated in this perspective.