Interval Structure: A Framework for Representing Uncertain Information
This work provides a theoretical framework for handling uncertainty in decision-making, but it appears incremental as it builds on existing models like rough sets and incidence calculus.
The paper tackles the problem of representing uncertain information by proposing a unified framework based on interval structures, showing that it can synthesize decision rules from experts and developing an efficient algorithm for rule selection.
In this paper, a unified framework for representing uncertain information based on the notion of an interval structure is proposed. It is shown that the lower and upper approximations of the rough-set model, the lower and upper bounds of incidence calculus, and the belief and plausibility functions all obey the axioms of an interval structure. An interval structure can be used to synthesize the decision rules provided by the experts. An efficient algorithm to find the desirable set of rules is developed from a set of sound and complete inference axioms.