REASONING UNDER UNCERTAINTY WITH BAYESIAN BELIEF NETWORKS ENHANCED WITH ROUGH SETS

Rough Sets for Uncertainty Reasoning

Rough sets have traditionally been applied to decision (classification) problems. We suggest that rough sets are even better suited for reasoning. It has already been shown that rough sets can be applied for reasoning about knowledge. In this preliminary paper, we show how rough sets provide a convenient framework for uncertainty reasoning. This discussion not only presents a new topic for futu...

Reasoning with Rough Sets and Paraconsistent Rough Sets

This thesis presents an approach to knowledge representation combining rough sets and paraconsistent logic programming. The rough sets framework proposes a method to handle a specific type of uncertainty originating from the fact that an agent may perceive different objects of the universe as being similar, although they may have different properties. A rough set is then defined by approximatio...

Reasoning under Uncertainty in Natural Language Dialogue using Bayesian Networks

A Framework for Reasoning with Rough Sets

Rough sets framework has two appealing aspects. First, it is a mathematical approach to deal with vague concepts. Second, rough set techniques can be used in data analysis to find patterns hidden in the data. The number of applications of rough sets to practical problems in different fields demonstrates the increasing interest in this framework and its applicability. Most of the current rough s...

A first-order logic for reasoning under uncertainty using rough sets

Reasoning with uncertain information is a problem of key importance when dealing with knowledge from real situations. Obtaining the precise numbers required by many uncertainty handling formalisms can be a problem when building real systems. The theory of rough sets allows us to handle uncertainty without the need for precise numbers, and so has some advantages in such situations. We develop a ...