Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Qualitative reasoning with imprecise probabilities
Journal of Intelligent Information Systems - Special issue: fuzzy logic and uncertainty management in information systems
Algorithms for precise and imprecise conditional probability assessments
Mathematical models for handling partial knowledge in artificial intelligence
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
A Logic for Order of Magnitude Reasoning with Negligibility, Non-closeness and Distance
Current Topics in Artificial Intelligence
Hi-index | 0.00 |
One of the simplest examples of information granulation is the use by humans of approximate equalities when reasoning with orders of magnitude. The paper proposes a symbolic approach for handling orders of magnitude in terms of a closeness relation and an associated negligibility relation. At the semantic level, these relations are represented by means of fuzzy sets and are parametered. A reduced set of rules, where the parameters are formally combined, embodies all the knowledge for reasoning on the basis of pieces of information in terms of orders of magnitude. These rules describe how closeness and negligibility relations can be composed and how they behave with respect to addition and product. The problem of handling qualitative probabilities in uncertain reasoning is then investigated in that perspective.