3.1.
Handbook of Defeasible Reasoning and Uncertainty
Management Systems
editors: Dov M. Gabbay and Philippe Smets
Volume 1 : Quantified Representation of
Uncertainty and Imprecision
edited by : Philippe Smets
Kluwer Academic Publishers, Dordrecht
* Hardbound, ISBN 0-7923-5100-2
July 1998, 484 pp.
NLG 399.00 / USD 215.00 / GBP 135.00
Prepublication price is valid until December 1, 1998
Table of contents
P. Smets
Probability, possibility, beliefs: which and where? 1
G. Panti
Multi-valued logics 25
V. Novak
Fuzzy logic 75
C. Howson
The bayesian approach 111
D. Gilles
Confirmation theory 135
D. Dubois and H. Prade
Possibility theory: qualitative and quantitative aspects 169
H. E. Kyburg, JR.
Families of probabilities 227
N. Sahlin and W. Rabinowicz
The evidentiary value model 247
P. Smets
The transferable belief model for quantified belief
representation 267
S. Benferhat
Infinitesimal theories of uncertainty for plausible
reasoning 303
A. W. F. Edwards
Statistical inference 357
J. Pearl
Graphical models for probabilistic and causal reasoning 367
B. Skyrms and P. Vanderschraaf
Game theory 391
G. Gigerenzer
Psychologoical challenges for normative models 441
3.2.
Handbook of Defeasible Reasoning and Uncertainty
Management Systems
editors: Dov M. Gabbay and Philippe Smets
Volume 3 : Belief Change
edited by : Didier Dubois and Henri Prade
Kluwer Academic Publishers, Dordrecht
* Hardbound, ISBN 0-7923-5162-2
August 1998, 458 pp.
NLG 380.00 / USD 199.00 / GBP 129.00
Prepublication price is valid until December 1, 1998
Table of Contents
. Introduction: revising, updating and combining knowledge
(D. Dubois, H. Prade)
. SYMBOLIC APPROACHES
a. Semantic approaches to the revision of propositional knowledge bases
(S.O. Hansson)
b. How hard is it to revise a belief base?
(B. Nebel)
c. Conditionals and the Ramsey test
(S. Lindstrom, W. Rabinowicz)
e. Logics for belief base updating
( A.Herzig)
f. The combination of knowledge bases
( L. Cholvy)
. NUMERICAL APPROACHES
a. Numerical representations of uncertainty
(P. Smets )
b.Belief change rules in ordinal and numerical uncertainty theories
(D. Dubois, S. Moral, H. Prade
c. Parallel combination of information sources
(R. Kruse & J. Gebhardt)
3.3.
Possibility Theory with Applications to Data Analysis
O.WOLKENHAUER,
Control Systems Centre, UMIST, UK
http://www.csc.umist.ac.uk/
Set theory and logic are the basic theoretical tools for modelling and
reasoning. Their application to real-world problems induces various types of
uncertainty related to the observation of processes, the measurement of
signals and the mismatch between mathematical models and the real world in
general. Possibility theory provides a framework in which all forms of
uncertainty can be represented. This book reviews, extends and applies
possibility theory in an integrated approach that combines probability
theory, statistical analysis and fuzzy mathematics.
Special features of the book include:
* An up-to-date introduction to possibility theory.
* An integrated view on uncertainty techniques based on multi-valued
mappings, fuzzy relations and random sets.
* Adoption of concepts into a temporal environment characterised by
signal or data processing.
* Illustration of the application of possibility theory to data analysis
in process and supervisory control systems with examples taken from the
area of condition monitoring.
CONTENTS: Motivation and Methodology. Uncertainties in Control Systems.
Uncertainty Techniques. Possibility Theory. Possibilistic Change Detection.
Fuzzy Data Analysis. Conclusions and Perspectives. Probability Theory.
Evidence Theory. Fuzzy Systems. Selected Topics. Glossary. Bibliography.
Index.
READERSHIP: Postgraduate Students interested in cutting edge topics related
to Fuzzy Systems; Researchers and Research Engineers in Control Engineering
- specifically in the area of data analysis for condition monitoring and
process control - data mining and data fusion applied to engineering
problems.
RSP SERIES: UMIST Control Systems Centre Series, No. 5
SERIES EDITORS: Dr M.B. Zarrop and Professor P.E. Wellstead, UMIST, UK
086380 229 X £55.00 January 1998 290pp
3.4.
Fuzzy Logic in Data Modeling
Semantics, Constraints, and Database Design
by
Guoqing Chen
Tsinghua University, Beijing, PR of China
THE KLUWER INTERNATIONAL SERIES ON ADVANCES IN
DATABASE SYSTEMS Volume 15
Kluwer Academic Publishers, Boston
hardbound, ISBN 0-7923-8253-6
August 1998, 240 pp.
NLG 260.00 / USD 115.00 / GBP 78.25
<<Fuzzy Lugic in Data Modeling: Semantics, Constraints and Database
Design>> addresses fundamental and important issues of fuzzy
data modeling, such as fuzzy data representation, fuzzy integrity
constraints, fuzzy conceptual modeling, and fuzzy database design.
The purpose of introducing fuzzy logic in data modeling is to
enhance the classical models such that uncertain and
imprecise information can be represented and manipulated.
Fuzzy data representation reflects how, where and to what extent
fuzziness is incorporated into classical models. Fuzzy integrity
constraints are a sort of fuzziness-involved business rules and
semantic restrictions that need to be specified and enforced.
Fuzzy conceptual modeling describes and treats high-level data
concepts and related semantics in a fuzzy context, allowing
the model to tolerate imprecision at different degrees. Fuzzy
database design provides guidelines for how relation schemes of
fuzzy databases should be formed and develops remedies to possible problems
of data redundancy and update anomalies.
Fuzzy Logic in Data Modeling£ºSemantics, Constraints and Database
Design is intended to be used as a text for a graduate-level
course on fuzzy databases, or as a reference for researchers and
practitioners in industry.
Contents:
Preface.
PART I: Basic Concepts
1. The Relational Data Model
2. Conceptual Modeling with the Entity-Relationship Model
3. Fuzzy Logic
PART II: Fuzzy Conceptual Modeling
4. Fuzzy ER Concepts
5. Fuzzy EER Concepts
PART III: Representation of Data and Constraints
6. Fuzzy Data Representation
7. Fuzzy Functional Dependencies as Integrity Constraints
8. A FFD Inference System
PART IV: Fuzzy Database Design and Information Maintenance
9. Scheme Decomposition and Information Maintenance
10.Design of Fuzzy Databases to Avoid Update Anomalies
Bibliography
Appendix
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