Crash Course in Artificial Intelligence and Expert Systems
Making computers more ueful by making them; Knowledge representation; An approach to problem solving; Introduction to expert systems; Developing an expert system; Natural language processing and voice recognition; Computer vision; Robotics and AI; programming in LISP; Prolog and other AI languages; AI hardware and the future of AI; Appendices; Index.
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An Approach to Problem Solving
Introduction to Expert Systems
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algorithm analyze applications architecture artificial intelligence backward chaining basic binary camera circuits complex computer vision computer vision systems computing speed converted create creature data base DBMS decision designed determine electronic example expert system expert system development facts Figure format frame function goal heuristic human IBM PC identify implemented inference engine input instructions key word knowledge base knowledge engineer knowledge representation LISP machines LISP programming logic look mainframes manipulate memory natural language front-end natural language interface natural language processing Neumann node objects operations output pattern matching perform personal computers pixels predicate premises primitives problem procedures processors production rules programming language Prolog questions reasoning represent RISC robot arm scene search tree semantic semantic networks sentence sequence signal simply Smalltalk solution solve specific speech stored supercomputers symbols techniques template understand variable various