Systematic Introduction to Expert Systems: Knowledge Representations and Problem-Solving MethodsAt 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. |
Contents
Use and Usability of Expert Systems | 17 |
4 | 27 |
6 | 41 |
construction and simulation are treated in detail First their domains problem | 46 |
Constraints | 51 |
Probabilistic Reasoning | 57 |
NonMonotonic Reasoning | 71 |
Temporal Reasoning | 79 |
Review of the ProblemSolving Type Construction 207 | 206 |
Skeletal Construction | 216 |
Propose and Revise | 223 |
Propose and Exchange | 229 |
LeastCommitment Strategy | 240 |
ModelBased Planning | 249 |
Partial Integration of Construction Methods | 255 |
Simulation | 263 |
Previous Approaches to Problem Classification | 89 |
Principles of ProblemSolving Methods | 101 |
Classification 113 | 115 |
Simple Classification | 128 |
Additional Mechanisms | 149 |
SetCovering Classification | 156 |
Functional Classification | 170 |
Statistical Classification | 183 |
CaseComparing Classification | 191 |
Qualitative MultiplePhase Simulation | 278 |
Integration of ProblemSolving Methods | 285 |
Integration of Classification Methods | 295 |
Aspects of the Overall Integration | 314 |
Survey of Knowledge Representation Formalisms | 322 |
| 332 | |
| 347 | |
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Systematic Introduction to Expert Systems: Knowledge Representations and ... Frank Puppe No preview available - 2011 |
Common terms and phrases
additional agenda algorithm application assignment attached procedures attributes backward chaining basic belief revision calculated case-comparing causal Chap complex concept configuration constraint propagation construction steps corresponding data abstractions data base decision tree default values derived diagnoses dialog domain efficient evaluation evidence values example expert system tools fault models Figure first-order predicate logic formula forward chaining functional models given global heuristic classification heuristic knowledge hierarchy hypotheses input intermediate justification KL-ONE knowledge acquisition knowledge base knowledge representation knowledge types loops materials MED2 MYCIN normal object types observations operators OPS5 output parameter values possible precondition predicate logic probabilities problem types problem-solving methods problem-solving types programming languages PROLOG propose-and-revise strategy propositional calculus propositions qualitative refinement relationships represented revision rule interpreter Sect selected set-covering similarity simple skeletal construction skeletal plan solution classes solving specific statistical structure suitable symptoms techniques temporal Theorem tion value range variables workpiece