Systematic Introduction to Expert Systems: Knowledge Representations and Problem-Solving Methods

Front Cover
Springer Science & Business Media, Dec 6, 2012 - Computers - 352 pages
At present one of the main obstacles to a broader application of expert systems is the lack of a theory to tell us which problem-solving methods areavailable for a given problem class. Such a theory could lead to significant progress in the following central aims of the expert system technique: - Evaluating the technical feasibility of expert system projects: This depends on whether there is a suitable problem-solving method, and if possible a corresponding tool, for the given problem class. - Simplifying knowledge acquisition and maintenance: The problem-solving methods provide direct assistance as interpretation models in knowledge acquisition. Also, they make possible the development of problem-specific expert system tools with graphical knowledge acquisition components, which can be used even by experts without programming experience. - Making use of expert systems as a knowledge medium: The structured knowledge in expert systems can be used not only for problem solving but also for knowledge communication and tutorial purposes. With such a theory in mind, this book provides a systematic introduction to expert systems. It describes the basic knowledge representations and the present situation with regard tothe identification, realization, and integration of problem-solving methods for the main problem classes of expert systems: classification (diagnostics), construction, and simulation.
 

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Contents

Characterization and History of Expert Systems
3
VI
11
VII
24
Basic Techniques of Knowledge Representation
27
Rules
35
ObjectsFrames
43
construction and simulation are treated in detail First their domains problem
46
Constraints
51
Functional Classification
170
Statistical Classification
183
CaseComparing Classification
191
Review of the ProblemSolving Type Construction 207
206
Skeletal Construction
216
Propose and Revise
223
Propose and Exchange
229
LeastCommitment Strategy
240

Probabilistic Reasoning
57
NonMonotonic Reasoning
71
Temporal Reasoning
79
Previous Approaches to Problem Classification
89
Principles of ProblemSolving Methods
101
Classification 113
115
Simple Classification
128
Additional Mechanisms
149
SetCovering Classification
156
ModelBased Planning
249
Qualitative MultiplePhase Simulation
278
Integration of Classification Methods
295
Aspects of the Overall Integration
314
Survey of Knowledge Representation Formalisms
322
References
332
System Index
347
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