Current issues in expert systems
Early work in artificial intelligence was mainly concerned with general problem-solving procedures. The application of these procedures to large "real world" problems was at first unsuccessful. Major progress was later made by allowing the problem-solving process to make use of separate, explicit sources of knowledge, concerning highly specific problem domains. This gave rise to a first generation of expert knowledge-based systems. The next step was to improve the power and flexibility of the various knowledge-processing techniques and to develop effective software and hardware architectures to implement them efficiently. This major challenge remains at the core of many of the "fifth-generation" computer projects.
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Logic for expert systems?
Summary and conclusions
separating the medical knowledge from
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advice-giving algorithm analysis apply Artificial Intelligence assertion attributes behaviour beliefs BigCo Brown bugs bytecode cache called Carbonell causal circuit Clancey clause command language complex components Concurrent PROLOG Conf conjuncts decomposition defined described developed diagnosis dialogue Diplococcus disease domain episode example expert systems explanation Figure front-end function game-theoretic goal GUIDON hardware HERACLES heuristics hypothesis idea implementation inference input Intelligent Tutoring Systems interaction interpreter John Seely Brown knowledge base knowledge representation learning LISP logic programming MACSYMA memory meningitis metarules method Michalski microcode microprocessor misconceptions MYCIN Neisseria NEOMYCIN operators output parameters play Plessey prediction problem problem-solving Proc procedure PROLOG propositions query Query-the-User question reasoning relation representation role rules selector semantic sequence simulation SMALLTALK SMALLTALK-80 solving SOPHIE SPARC/G specific stack strategy structure student modelling Sword32 t-rules task teaching tetracycline theory tion understand user's variables workstations