Knowledge Based Systems: Advanced Concepts, Techniques & ApplicationsThe field of knowledge-based systems (KBS) has expanded enormously during the last years, and many important techniques and tools are currently available. Applications of KBS range from medicine to engineering and aerospace.This book provides a selected set of state-of-the-art contributions that present advanced techniques, tools and applications. These contributions have been prepared by a group of eminent researchers and professionals in the field.The theoretical topics covered include: knowledge acquisition, machine learning, genetic algorithms, knowledge management and processing under uncertainty, conflict detection and resolution, structured knowledge architectures, and natural language-based man-machine communication.The Applications include: Real-time decision support, system fault diagnosis, quality assessment, manufacturing production, robotic assembly, and robotic welding.The reader can save considerable time in searching the scattered literature in the field, and can find here a powerful set of how-to-do issues and results. |
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Contents
Chapter I | 3 |
Review of Psychological Knowledge | 10 |
Know ledge Acquisition Through Concept | 17 |
Knowledge Acquisition | 25 |
Knowledge Acquisition as Modelling | 31 |
Review of Automated Knowledge Acquisition Tools | 43 |
Conclusions | 49 |
A Survey | 61 |
GeneticsBased Learning and Statistical Generalization | 319 |
Generalization Across Multiple Subdomains | 334 |
Experimental Results | 340 |
Chapter 12 | 351 |
Design | 363 |
Conclusions | 371 |
Basic Assumptions | 377 |
Integration of CBR with Model Based Reasoning | 384 |
Machine Learning and Related Fields | 75 |
Conclusions | 83 |
Version Space Learning Family | 96 |
The Explanation Based Learning Family | 102 |
Genetic Algorithm Based Inductive Learning Family | 110 |
Conclusions | 118 |
Chapter 4 | 125 |
Knowledge Management Within | 132 |
Conclusions | 154 |
Representation Schemes | 161 |
Nonmonotonic Reasoning | 165 |
Uncertainty in Procedural Knowledge | 186 |
Symbolic Approaches | 192 |
Revision as Numerical Treatment of Uncertainty | 199 |
Revision in a MultiAgent Environment | 208 |
Chapter 7 | 217 |
The KSM Methodology with Examples | 226 |
KSM Operation | 237 |
Chapter 8 | 247 |
Speculative Computation of Production Systems | 254 |
Experimental Results and Discussion | 261 |
Chapter 9 | 269 |
Examples | 283 |
Comparison | 289 |
Brief Review of Results of Previous Analysis | 295 |
Extended Graph Tuple | 302 |
Summary and Conclusions | 316 |
Discussion | 392 |
Chapter 14 | 399 |
Algorithms | 406 |
An Illustrative Example | 414 |
Background | 421 |
CaseBased Reasoning CBR | 427 |
Conclusions | 437 |
Chapter 17 | 439 |
Chapter 16 | 443 |
Application of the Expert System | 450 |
Discussion of Results | 457 |
Knowledge Based Systems | 465 |
The Arc Welding Domain in Knowledge Engineering | 472 |
Conclusions | 492 |
The General Reasoning Method for Decision Support | 499 |
Summary of a Decision Support Knowledge Environment | 514 |
Conclusions | 522 |
Basic Concepts for a Distributed Network of Controllers | 529 |
Chapter 20 | 539 |
Manufacturing Environment for Testing | 551 |
Architectural and Functional Issues of a Decision Support | 559 |
Requirements from a System to Support Concurrent Design | 566 |
Conclusions | 584 |
Conclusions and Application Issues | 600 |
615 | |
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Common terms and phrases
action analysis application approach Artificial Intelligence Axiomatic Design axioms baseline HM basic belief revision Case-Based Case-Based Reasoning causal chapter complex components Computer concept learning conflict detection consistent constraints decision defined diagnosis domain elements environment evaluation example expert systems Figure formal framework function fuzzy Genetic Algorithm goal gyadesc heuristic IEEE implementation inductive learning inference input instance interaction interview knowledge acquisition knowledge areas knowledge engineer knowledge representation knowledge structure knowledge unit knowledge-based system language learning systems logic machine learning match methodology methods modules node ontologies operation Ops5 parallel parameters performance plan network possible probabilistic problem solving Proc procedures processors production systems proposed reasoning repertory grid representation represented Robot rules selection sentences sequence solution specification step subdomains subset task techniques tool tuple uncertainty values variables version space
References to this book
Co-ordination in Artificial Agent Societies: Social Structures ..., Issue 1535 Sascha Ossowski No preview available - 1999 |
Computational Intelligence in Systems and Control Design and Applications S.G. Tzafestas Limited preview - 1999 |