UID:
almafu_9959230372302883
Format:
1 PDF (xviii, 506 pages) :
,
illustrations.
ISBN:
0-262-29330-7
,
0-262-25677-0
,
0-585-49022-8
Series Statement:
Artificial Intelligence
Content:
In this book Simon Parsons describes qualitative methods for reasoning under uncertainty, "uncertainty" being a catch-all term for various types of imperfect information. The advantage of qualitative methods is that they do not require precise numerical information. Instead, they work with abstractions such as interval values and information about how values change. The author does not invent completely new methods for reasoning under uncertainty but provides the means to create qualitative versions of existing methods. To illustrate this, he develops qualitative versions of probability theory, possibility theory, and the Dempster-Shafer theory of evidence. According to Parsons, these theories are best considered complementary rather than exclusive. Thus the book supports the contention that rather than search for the one best method to handle all imperfect information, one should use whichever method best fits the problem. This approach leads naturally to the use of several different methods in the solution of a single problem and to the complexity of integrating the results--a problem to which qualitative methods provide a solution.
Note:
Bibliographic Level Mode of Issuance: Monograph
,
Machine generated contents note: 1 Introduction 1 -- 2 All about uncertainty 7 -- 2.1 Introduction 7 -- 2.2 Taxonomies of uncertainty 9 -- 2.3 Sources of imperfect information 15 -- 2.4 Uncertainty and entropy 18 -- 2.5 Human reasoning under uncertainty 20 -- 2.6 Ground rules for formal systems 29 -- 2.7 Summary 34 -- 3 Quantitative methods for reasoning with imperfect information 37 -- 3.1 Introduction 38 -- 3.2 The main models 39 -- 3.3 Other important models 65 -- 3.4 Computational techniques 73 -- 3.5 Quantified logics 97 -- 3.6 Summary 105 -- 4 Qualitative methods for reasoning with imperfect information 107 -- 4.1 Introduction 108 -- 4.2 Qualitative physics 109 -- 4.3 Interval-based systems 117 -- 4.4 Abstractions of quantitative systems 123 -- 4.5 Defeasible reasoning 134 -- 4.6 Combining and relating formalisms 155 -- 4.7 Summary 166 -- 5 A framework for studying different methods 169 -- 5.1 Introduction 169 -- 5.2 Eclecticism and the integration problem 172 -- 5.3 A general framework 184 -- 5.4 Examples of integration and incompleteness 191 -- 5.5 Summary 199 -- 6 Using qualitative algebras 201 -- 6.1 Introduction 201 -- 6.2 An algebra with qualitative values 202 -- 6.3 An algebra of interval values 209 -- 6.4 Other qualitative algebras 219 -- 6.5 An example of handling integration 221 -- 6.6 An example of handling incompleteness 228 -- 6.7 Summary 233 -- 7 The theory of qualitative change 237 -- 7.1 Introduction 237 -- 7.2 Basic concepts of qualitative change 239 -- 7.3 Causal reasoning 247 -- 7.4 Evidential reasoning 263 -- 7.5 Handling incompleteness and integration 273 -- 7.6 Summary 280 -- 8 Further results in the theory of qualitative change 283 -- 8.1 Synergy 283 -- 8.2 Propagation in multiply-connected networks 296 -- 8.3 Intercausal reasoning 311 -- 8.4 Related work 322 -- 8.5 Summary 327 -- 9 Implementing the qualitative approaches 329 -- 9.1 Introduction 330 -- 9.2 Implementing qualitative algebras 330 -- 9.3 Implementing the theory of qualitative change 336 -- 9.4 Summary 351 -- 10 Qualitative protein topology prediction 353 -- 10.1 Introduction 354 -- 10.2 Protein topology prediction 356 -- 10.3 A first approach to modelling the uncertainty 358 -- 10.4 A second approach to modeling the uncertainty 373 -- 10.5 Discussion 387 -- 10.6 Summary 389 -- 11 Summary and conclusions 391 -- 11.1 Summary 391 -- 11.2 Conclusions 394.
,
Also available in print.
,
English
Additional Edition:
ISBN 0-262-52874-6
Additional Edition:
ISBN 0-262-16168-0
Language:
English
URL:
OCLC metadata license agreement
