Frontiers of Expert Systems: Reasoning With Limited KnowledgeSpringer Science & Business Media, 2000 M05 31 - 303 pages The development of modern knowledge-based systems, for applications ranging from medicine to finance, necessitates going well beyond traditional rule-based programming. Frontiers of Expert Systems: Reasoning with Limited Knowledge attempts to satisfy such a need, introducing exciting and recent advances at the frontiers of the field of expert systems. Beginning with the central topics of logic, uncertainty and rule-based reasoning, each chapter in the book presents a different perspective on how we may solve problems that arise due to limitations in the knowledge of an expert system's reasoner. Successive chapters address (i) the fundamentals of knowledge-based systems, (ii) formal inference, and reasoning about models of a changing and partially known world, (iii) uncertainty and probabilistic methods, (iv) the expression of knowledge in rule-based systems, (v) evolving representations of knowledge as a system interacts with the environment, (vi) applying connectionist learning algorithms to improve on knowledge acquired from experts, (vii) reasoning with cases organized in indexed hierarchies, (viii) the process of acquiring and inductively learning knowledge, (ix) extraction of knowledge nuggets from very large data sets, and (x) interactions between multiple specialized reasoners with specialized knowledge bases. Each chapter takes the reader on a journey from elementary concepts to topics of active research, providing a concise description of several topics within and related to the field of expert systems, with pointers to practical applications and other relevant literature. Frontiers of Expert Systems: Reasoning with Limited Knowledge is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry. |
Contents
KnowledgeBased Systems | 1 |
11 Early Expert Systems | 2 |
12 Roles Tasks Applications | 4 |
13 Structure of an Expert System | 5 |
14 Knowledge Representation | 6 |
15 To use or not to use? | 7 |
16 Verification and Validation | 10 |
17 The rest of this book | 12 |
Bibliography | 173 |
65 Exercises | 175 |
Case Based Reasoning Systems | 177 |
71 Overview | 178 |
72 Retrieval | 180 |
73 Adaptation | 183 |
731 Derivational Adaptation | 184 |
732 Structural Adaptation | 185 |
18 Bibliographic Notes | 15 |
Bibliography | 17 |
Practical Reasoning | 19 |
21 Formal Inference | 20 |
212 Inference Rules | 21 |
213 Semantics | 23 |
22 Temporal Logic | 25 |
23 NonMonotonic Reasoning | 28 |
24 Truth Maintenance | 31 |
25 Model Based Reasoning | 33 |
26 Bibliographic Notes | 35 |
Bibliography | 37 |
27 Exercises | 39 |
Uncertainty | 41 |
31 Probability | 42 |
32 Likelihoods of Sufficiency and Necessity | 45 |
33 Probabilistic Inference Networks | 48 |
34 Interpolating Conditional Probabilities | 51 |
35 Combining Evidence | 55 |
36 Logical Inferences in Probabilistic Networks | 60 |
37 Cycles and Multiple Dependencies | 63 |
38 Reasoning in Acyclic Networks | 66 |
39 Decision Theory and Utilities | 75 |
392 Utility Theory | 78 |
310 DempsterShafer Calculus | 82 |
3102 Combining Evidence | 83 |
311 Fuzzy Systems | 85 |
312 Certainty Factors | 87 |
313 Bibliographic Notes | 89 |
Bibliography | 91 |
314 Exercises | 93 |
Rule Based Programming | 99 |
41 Grammar Rules | 100 |
42 Rewrite Rules | 101 |
421 Petri Nets | 102 |
43 Ordering the rules | 103 |
44 Backward ho | 105 |
45 Production Rules | 107 |
46 Inference Engine | 113 |
47 Matching | 114 |
48 Conflict Resolution | 118 |
49 Specifying and Verifying Rules | 120 |
491 Reasoning about actions | 122 |
493 Variables | 124 |
410 Bibliographic Notes | 125 |
Bibliography | 127 |
Evolving Classifiers | 133 |
51 Learning Classifier Systems | 134 |
52 Representation | 136 |
53 Rule Firing | 138 |
54 Credit Allocation | 140 |
55 Rule Discovery | 143 |
56 Grouping Rules | 145 |
57 Examples of Classifier Systems | 147 |
58 Bibliographic Notes | 150 |
Bibliography | 153 |
Connectionist Systems | 157 |
61 Neural Networks | 158 |
611 Node Functions | 159 |
612 Network Architecture | 160 |
613 Neural Learning | 161 |
614 Connectionism and Expert Systems | 163 |
63 MACIE | 166 |
64 Bibliographic Notes | 171 |
74 Case Library | 186 |
741 Constructing the Case Library | 187 |
742 Hierarchical Organization of Cases | 188 |
75 Interfaces and Feedback | 189 |
76 Case Based Learning | 190 |
77 Examples | 191 |
78 Analogical Reasoning | 193 |
79 Bibliographic Notes | 195 |
Bibliography | 197 |
710 Exercises | 199 |
Knowledge Acquisition | 201 |
81 Key Concerns | 202 |
8112 Difficulty | 203 |
8114 Resistance | 204 |
812 Procedure | 205 |
82 Interacting with Experts | 208 |
821 Unstructured Meetings | 209 |
823 Delphi Method | 210 |
826 Case Studies | 211 |
8262 Observational Case Study | 212 |
831 Grid Analysis | 213 |
832 Logic of Confirmation | 217 |
833 Rule Generation Procedure | 218 |
84 Induction of Knowledge | 219 |
841 Splitting | 221 |
842 Multiclass problems | 227 |
844 Multivalued attributes | 228 |
845 Computational Cost | 229 |
85 Bibliographic Notes | 231 |
Bibliography | 233 |
86 Exercises | 235 |
Data Mining | 237 |
91 Preprocessing | 238 |
912 Noise | 239 |
913 Missing Values | 240 |
92 Transforming Representations | 241 |
923 Data Reduction | 242 |
93 Knowledge Discovery | 244 |
931 Classification Trees | 245 |
933 Association Rules | 247 |
94 Prediction | 252 |
95 Bibliographic Notes | 254 |
Bibliography | 255 |
Distributed Experts | 259 |
101 Distributed Artificial Intelligence | 260 |
102 Blackboard Systems | 263 |
1021 Hearsay | 265 |
1022 HASP | 270 |
1023 GBB | 271 |
1024 DVMT | 272 |
1025 BB1 | 273 |
103 Multiagent Systems | 274 |
1031 MAS Architectures | 275 |
104 Agent Interactions | 278 |
1042 Agent coordination protocols | 283 |
1043 Cooperation protocols | 285 |
1044 Negotiation | 287 |
105 Example Applications | 288 |
1052 Emergency Management | 289 |
106 Bibliographic Notes | 290 |
291 | |
107 Exercises | 294 |
297 | |
Other editions - View all
Frontiers of Expert Systems: Reasoning with Limited Knowledge Chilukuri Krishna Mohan Limited preview - 2012 |
Frontiers of Expert Systems: Reasoning with Limited Knowledge Chilukuri Krishna Mohan No preview available - 2013 |
Common terms and phrases
adaptation agents analysis applied approach Artificial Intelligence associated assumptions attribute blackboard systems case-based reasoning CBR system Chapter classification tree classifier systems clustering components computational conditional independence connectionist connectionist expert system contains context data elements data mining database decision denote described determine developed discussed domain evaluate evidence Example execution Figure formulas function fuzzy Genetic Algorithms Global_clock goal hypotheses ID3 algorithm inference engine inference rules input instance interaction International Conf involves itemsets knowledge acquisition Knowledge Discovery KQML language layer learning algorithm machine learning MACIE matching modify multiple neural networks node obtained output patterns payoff perform personal constructs possible posterior probability probabilistic inference network problem problem-solving Proc programming protocols relevant repertory grid represent representation retrieved rule-based expert systems rule-based programming rule-based system Section sequence solution solve specific strategy structure subsets task temporal logic tion Truth maintenance systems values variables
References to this book
Intelligentes Missionsmanagement für autonome mobile Systeme Torsten Pfützenreuter Limited preview - 2005 |