Frontiers of Expert Systems
Reasoning with Limited Knowledge
Published on 6. December 2012
XII, 303 pages
978-1-4615-4509-5 (ISBN)
Description
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.
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.
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Content
1 Knowledge-Based Systems.- 1.1 Early Expert Systems.- 1.2 Roles, Tasks, Applications.- 1.3 Structure of an Expert System.- 1.4 Knowledge Representation.- 1.5 To use, or not to use?.- 1.6 Verification and Validation.- 1.7 The rest of this book.- 1.8 Bibliographic Notes.- 2 Practical Reasoning.- 2.1 Formal Inference.- 2.2 Temporal Logic.- 2.3 Non-Monotonic Reasoning.- 2.4 Truth Maintenance.- 2.5 Model Based Reasoning.- 2.6 Bibliographic Notes.- 2.7 Exercises.- 3 Uncertainty.- 3.1 Probability.- 3.2 Likelihoods of Sufficiency and Necessity.- 3.3 Probabilistic Inference Networks.- 3.4 Interpolating Conditional Probabilities.- 3.5 Combining Evidence.- 3.6 Logical Inferences in Probabilistic Networks.- 3.7 Cycles and Multiple Dependencies.- 3.8 Reasoning in Acyclic Networks.- 3.9 Decision Theory and Utilities.- 3.10 Dempster-Shafer Calculus.- 3.11 Fuzzy Systems.- 3.12 Certainty Factors.- 3.13 Bibliographic Notes.- 3.14 Exercises.- 4 Rule Based Programming.- 4.1 Grammar Rules.- 4.2 Rewrite Rules.- 4.3 Ordering the rules.- 4.4 Backward ho!.- 4.5 Production Rules.- 4.6 Inference Engine.- 4.7 Matching.- 4.8 Conflict Resolution.- 4.9 Specifying and Verifying Rules.- 4.10 Bibliographic Notes.- 4.11 Exercises.- 5 Evolving Classifiers.- 5.1 Learning Classifier Systems.- 5.2 Representation.- 5.3 Rule Firing.- 5.4 Credit Allocation.- 5.5 Rule Discovery.- 5.6 Grouping Rules.- 5.7 Examples of Classifier Systems.- 5.8 Bibliographic Notes.- 6 Connectionist Systems.- 6.1 Neural Networks.- 6.2 KBCNN.- 6.3 MACIE.- 6.4 Bibliographic Notes.- 6.5 Exercises.- 7 Case Based Reasoning Systems.- 7.1 Overview.- 7.2 Retrieval.- 7.3 Adaptation.- 7.4 Case Library.- 7.5 Interfaces and Feedback.- 7.6 Case Based Learning.- 7.7 Examples.- 7.8 Analogical Reasoning.- 7.9 Bibliographic Notes.- 7.10 Exercises.-8 Knowledge Acquisition.- 8.1 Key Concerns.- 8.2 Interacting with Experts.- 8.3 Personal Construct Technology.- 8.4 Induction of Knowledge.- 8.5 Bibliographic Notes.- 8.6 Exercises.- 9 Data Mining.- 9.1 Preprocessing.- 9.2 Transforming Representations.- 9.3 Knowledge Discovery.- 9.4 Prediction.- 9.5 Bibliographic Notes.- 10 Distributed Experts.- 10.1 Distributed Artificial Intelligence.- 10.2 Blackboard Systems.- 10.3 Multiagent Systems.- 10.4 Agent Interactions.- 10.5 Example Applications.- 10.6 Bibliographic Notes.- 10.7 Exercises.
