Classics



Understanding natural language

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This paper describes a computer system for understanding English. It is based on the belief that in modeling language understanding, we must deal in an integrated way with all of the aspects of language--syntax, semantics, and inference. It enters into a dialog with a person, responding to English sentences with actions and English replies, asking for clarification when its heuristic programs cannot understand a sentence through the use of syntactic, semantic, contextual, and physical knowledge. By developing special procedural representations for syntax, semantics, and inference, we gain flexibility and power.


The technology chess program

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See also: Performance Analysis of the Technology Chess Program. Carnegie Mellon University interim report citation at DTIC OnlineArtificial Intelligence 3:145-163


Semantic categories of nominals for conceptual dependency analysis of natural language

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Abstract: A system for the semantic categorization of conceptual objects (nominals) is provided. The system is intended to aid computer understanding of natural language. Specific implementations for noun-pairs and prepositional phrases are offered.


Some techniques for proving correctness of programs which alter data structures

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We will extend Floyd's proof system for flow diagrams to handle commands Which process lists. McCarthy and Painter (1967) deal with arrays by introducing'change' and'access' functions so as to write a[i]: a[j] 1 as a: change (a, i, access 24 BURSTALL King (1969) in mechanising Floyd's technique gives a method for such assignments which, however, introduces case analysis that sometimes becomes unwieldy. Let us recall briefly the technique of Floyd (1967) for proving correctness of programs in flow diagram form. We will here retain the inductive method of Floyd for dealing with flow diagrams containing loops, but give methods for coping with more complex kinds of assignment command.


Learning and executing generalized robot plans

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"In this paper we describe some major new additions to the STRIPS robot problem-solving system. The first addition is a process for generalizing a plan produced by STRIPS so that problem-specific constants appearing in the plan are replaced by problem-independent parameters.The generalized plan, stored in a convenient format called a triangle table, has two important functions. The more obvious function is as a single macro action that can be used by STRIPS—either in whole or in part—during the solution of a subsequent problem. Perhaps less obviously, the generalized plan also plays a central part in the process that monitors the real-world execution of a plan, and allows the robot to react "intelligently" to unexpected consequences of actions.We conclude with a discussion of experiments with the system on several example problems."Artificial Intelligence 3:251-288


QA4: A procedural calculus for intuitive reasoning

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Abstract: This report presents a language, called QA4, designed to facilitate the construction of problem-solving systems used for robot planning, theorem proving, and automatic program synthesis and verification. Thus it provides many useful programming aids. More importantly, however, it provides a semantic framework for common sense reasoning about these problem domains. The interpreter for the language is extraordinarily general, and is therefore an adaptable tool for developing the specialized techniques of intuitive, symbolic reasoning used by the intelligent systems.