Rep. No. TM-86, Laboratory for Computer Science, Massachusetts Institute of Technology.

IJCAI 5, 928-932.

"The epistemological part of Al studies what kinds of facts about the world are available to an observer with given opportunities to observe, how these facts can be represented in the memory of a computer, and what rules permit legitimate conclusions to be drawn from these facts. It leaves aside the heuristic problems of how to search spaces of possibilities and how to match patterns."See also: IJCAI 5, 1038-1044In Readings in Artificial Intelligence, B.L. Webber and N.J. Nilsson (eds.), Tioga Publishing, 1981.

This paper presents a functional overview of the features and capabilities of QLISP, one of the newest of the current generation of very high level languages developed for use in Artificial Intelligence (AI) research.QLISP is both a programming language and an interactive programming environment. It embeds an extended version of QA4, an earlier AI language, in INTERLISP, a widely available version of LISP with a variety of sophisticated programming aids.The language features provided by QLISP include a variety of useful data types, an associative data base for the storage and retrieval of expressions, the ability to associate property lists with arbitrary expressions, a powerful pattern matcher based on a unification algorithm, pattern-directed function invocation, "teams" of pattern invoked functions, a sophisticated mechanism for breaking a data base into contexts, generators for associative data retrieval, and easy extensibility.System features available in QLISP include a very smooth interaction with the underlying INTERLISP language, a facility for aggregating multiple pattern matches, and features for interactive control of programs.A number of applications to which QLISP has been put are briefly discussed, and some directions for future development are presented. SRI Tech.Note 120, AI Center, SRI International, Inc., Menlo Park, Calif.

The alpha-beta technique for searching game trees is analyzed, in an attempt to provide some insight into its behavior. The first portion of this paper is an expository presentation of the method together with a proof of its correctness and a historical discussion. The alpha-beta procedure is shown to be optimal in a certain sense, and bounds are obtained for its running time with various kinds of random data.

This paper suggests a bidirectional relationship between state-space and problem-reduction representations. It presents a formalism based on multiple-input and multiple-output operators which provides a basis for viewing the two types of representations in this manner. A representation of the language recognition problem which is based on the Cocke parsing algorithm is used as an illustration. A method for representing problems in first-order logic in such a way that the inference system employed by a resolution-based theorem prover determines whether the set of clauses is interpreted in the state-space mode or in the problem-reduction mode is presented. The analogous concepts in problem-reduction and theorem proving, and the terminology used to refer to them, are noted. The relationship between problem-reduction, input resolution, and linear resolution is is discussed.

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Also see book version (1980), IJCAI-77. Also Rep. No. TR-347, AI Laboratory, Massachusetts Institute of Technology.

John Wiley and Sons, Inc. Robinson, J.A. (1965) "A Machine Oriented Logic Based on the Resolution Principle".

IJCAI S, 23-29.