Frontiers of Expert Systems: Reasoning with Limited Knowledge
Springer Science & Business Media, 2012 M12 6 - 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.
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adaptation agents analysis applied approach Artificial Intelligence associated assumptions blackboard systems case-based reasoning CBR system changes Chapter classification tree classifier systems clustering components computational conditional independence connectionist expert system contains context corresponding 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 identifying inference engine inference rules input instance interaction International Conf involves itemsets knowledge acquisition knowledge base KQML language layer learning algorithm machine learning matching modify multiple neural networks node obtained output patterns payoff perform personal constructs possible posterior probability prediction probabilistic inference network problem problem-solving Proc programming relevant repertory grid represent representation require retrieved rule-based expert systems rule-based programming rule-based system Section sequence solution solve specific strategy string structure subsets task temporal logic tion Truth maintenance systems variables