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References
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Green, L., Berkeley, E., and Gotlieb, C.C. (1959), Conversation with a computer, Computers and Automation 8, 9–11. This is a very early system which answered questions about the weather.
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Lindsay, R. (1963), Inferential memory as the basis of machines which understand natural language, in: E. Feigenbaum and J. Feldman, Eds., Computers and Thought (McGraw-Hill, New York) 217–233. A very early and (by today's standards) tiny system that explored question-answering by machine. The program, limited to an 850-word vocabulary of Basic English, incorporated design features which have subsequently gained acceptance in the AI community, e.g., goal orientation, heuristics in parsing, stress on inference.
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McDermott, D. (1978), The last survey of knowledge representation, Proceedings of the AISB/GB Conference on AI, Hamburg, 206–221. A survey of current issues in representation theory with an evaluation of what needs to be done. McDermott emphasizes the development of representation ‘techniques’ and discusses representation in terms of programming, semantics, ‘new’ notations, and managing dynamic data bases.
McDermott, D. (1978b), Tarskian semantics, or No Notation Without Denotation, Cognitive Science 2, 277–282. Explaining how denotational semantics is a tool for representation research McDermott defends it against various arguments. The paper is clear and direct, written for those who have yet to discover the utility of denotational semantics. True believers will see in this paper, a “now I'm convinced, what about you guys” attitude, which would be a good introduction to Hayes (1974, 1977).
McDermott, D. and Doyle, J. (1979), Non-Monotonic Logic I, AI Lab Memo 489A, MIT, Cambridge, MA. Based on logic in which new axioms can invalidate old theorems, a system is proposed as a model of beliefs, and its correspondence to some related systems is discussed.
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Pople, H. (1977), The Formation of Hypothesis in Diagnostic Problem Solving, and Exercise in Synthetic Reasoning, Proceedings IJCAI5, Cambridge, MA, 1030–1037. This paper reports the previous seven years' progress on a major system which William Meyers, a world-renowned diagnostician, believes duplicates his diagnostic abilities.
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Raphael, B. (1968), SIR: A Computer Program for Semantic Information Retrieval, in: M. Minsky, Ed. (MIT Press, Cambridge, MA) 33–145. Raphael's system builds a memory structure to convert the information it receives into a systematic, efficient representation, a network in which objects are interconnected by a variety of different relations.
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Winograd, T. (1972), Understanding Natural Language (Academic Press, New York). Winograd's language understanding program endorses the procedural meaning representation for knowledge due to Hewitt (1971). In his system, which combines many fascinating techniques to produce spectacular results, semantic analysis takes place before syntactic processing is complete. This was the first true integration of semantic and syntactic processing to produce good results. As they relate to a more general understanding paradigm, however, Winograd's methods received many criticisms.
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Nick Cercone is in the computing science department of Simon Fraser University, Bristih Columbia. Randy Goebel is in the computing science department of Waterloo University, Ontario, Canada.
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Cercone, N., Goebel, R. Data bases and knowledge representation for literary and linguistic studies. Comput Hum 17, 121–137 (1983). https://doi.org/10.1007/BF02259885
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DOI: https://doi.org/10.1007/BF02259885