Prolog is a simple but powerful programming language founded on symbolic logic. The basic computational mechanism is a pattern matching process ("unification") operating on general record structures ("terms" of logic). We briefly review the language and compare it especially with pure Lisp. The remainder of the paper discusses techniques for implementing Prolog efficiently; in particular we describe how to compile the patterns involved in the matching process. These techniques are as incorporated in our DECsystem-10 Prolog compiler (written in Prolog).

Planning is the process of formulating an intended course of action. In this paper we present a model of planning and describe the current version of an INTERLISP simulation of the model. We also review psychological results which confirm the model's basic assumptions for human planning behavior.

This paper describes an investigation of the feasibility of resolving anaphors in natural language texts by means of a'shallow processing' approach which exploits knowledge of syntax, semantics and local focussing as heavily as possible; it does not rely on the presence of large amounts of world or domain knowledge, which are notoriously hard to process accurately. The ideas reported are implemented in a program called SPAR (Shallow Processing Anaphor Resolver), which resolves anaphoric ambiguities in simple English stories and generates sentence-by-sentence paraphrases that show what interpretations have been selected. To resolve anaphors, SPAR combines and develops several existing techniques, most notably Sidner's theory of local focussing and Wilks' 'preference semantics' theory of semantics and common sense inference Consideration of the need to resolve several anaphors in the same sentence results in Sidner's framework being modified and extended to allow focus-based processing to interact more flexibly with processing based on other types of knowledge. Wilks' treatment of common sense inference is extended to incorporate a wider range of types of inference without jeopardizing its uniformity and simplicity. In the absence of large quantities of world knowledge, successful anaphor resolution is seen to depend on the coordination of predictions made by system components exploiting various knowledge sources.

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This paper discusses an effort in the application of artificial intelligence to the access of data from a large, distributed data base over a computer network. A running system is described that provides real-time access over the ARPANET to a data base distributed over several machines. The system accepts a rather wide range of natural language questions about the data, plans a sequence of appropriate queries to the data base management system to answer the question, determines on which machine(s) to carry out the queries, establishes links to those machines over the ARPANET, monitors the prosecution of the queries and recovers from certain errors in execution, and prepares a relevant answer. In addition to the components that make up the demonstration system, more sophisticated functionally equivalent components are discussed and proposed. The work described in this paper represents the joint efforts of an integrated, energetic group at SRI. Members of this group include Rich Fikes (now at Xerox PARC), Koichi Furukawa (now at ETL).

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Book-length version of the central ideas presented in Roger C. Schank and Robert P. Abelson, "SCRIPTS, PLANS, AND KNOWLEDGE" http://oak.conncoll.edu/parker/com316/progassign/scripts.pdf Hillsdale, NJ: Lawrence Erlbaum Associates

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SIGART Newsletter 63:33-37