Titlebook: Uncertainty Management in Information Systems; From Needs to Soluti Amihai Motro,Philippe Smets Book 1997 Springer Science+Business Media N

Insensate

Introduction,ated with uncertainty. This subject is at the intersection of two areas of knowledge: . is an area that concentrates on the design of practical systems that can store and retrieve information; . is an area in artificial intelligence concerned with accurate representation of uncertain information and

magnate

Sources of Uncertainty, Imprecision, and Inconsistency in Information Systems,of abstraction that is implied by the expected applications. As with any other model, the most important consideration is the integrity of the model; i.e., the accuracy of the representation. Unfortunately, our knowledge of the real world is often imperfect, thus challenging our ability to create an

同步信息

groggy

Uncertainty in Information Retrieval Systems,ant. We are generally provided with a description of the user’s information need, or query. We then select documents likely to be relevant by comparing the query with prestored document representations, and may, optionally, revise the query representation for subsequent retrieval. The retrieval proc

和諧

機(jī)制

Probabilistic and Bayesian Representations of Uncertainty in Information Systems: A Pragmatic Introayesian methods. While we cannot entirely resist discussing basic principles, we will focus on the pragmatic issues, which too often get lost under the mass of philosophy and mathematics. We will address such questions as: How can we use probability to represent the various types of uncertainty? How

雕鏤

An Introduction to the Fuzzy Set and Possibility Theory-Based Treatment of Flexible Queries and Unce available information is imperfect, it is often desirable to try to represent it in the database nonetheless, so that it can be used to answer queries of interest as much as possible. A related issue is the handling of imperfect or flexible queries. For example, a natural query language may use a

一再遛

Logical Handling of Inconsistent and Default Information,le) information. In many information system applications, there is a need to represent and reason with inconsistent data. For example, in a tax collection agency, database records on individual taxpayers should be allowed to have inconsistent information, as such information could be used to direct

驚惶

The Transferable Belief Model for Belief Representation,ing combined forms of imperfect data has hardly been achieved. It would nevertheless seem useful to have a single model that could represent several forms of uncertainty. Possibly, this could be achieved by simulating what might be the human approach to such problems.

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