To use language one must be able to make inferences about the information which language conveys. This is apparent in many ways. For one thing, many of the processes which we typically consider "linguistic" require inference making. For example, structural disambiguation: (1) Waiter, I would like spaghetti with meat sauce and wine. You would not expect to be served a bowl of spaghetti floating in meat sauce and wine. That is, you would expect the meal represented by structure (2) rather than that represented by (3).

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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Machine intelligence problems are sometimes defined as those problems which (i) computers can't yet do, and (ii) humans can. We shall further consider how much "knowledge" about a finite mathematical function can, on certain assumptions, be credited to a computer program. Although our approach is quite general, we are really only interested in programs which evaluate "semihard" functions, believing that the evaluation of such functions constitutes the defining aspiration of machine intelligence work. If a function is less hard than "semihard," then we can evaluate it by pure algorithm (trading space for time) or by pure lookup (making the opposite trade), with no need to talk of knowledge, advice, machine intelligence, or any of those things. We call such problems "standard." If however the function is "semihard," then we will be driven to construct some form of artful compromise between the two representations: without such a compromise the function will not be evaluable within practical resource limits. If the function is harder than "semihard," i.e. is actually "hard," then no amount of compromise can ever make feasible its evaluation by any terrestrial device.

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).

How can we get a computer to understand natural language? Our view of the problem has progressed over the years to a point where an answer to that question today would look quite different from one given ten or even five years ago. Originally, researchers felt that the most relevant issue was syntax. Later, most people agreed that semantics was the most relevant field of study (although few would have agreed on what semantics was). Five years ago, or so, our research was concentrated on finding an adequate meaning representation for sentences.

This paper is a preliminary proposal to develop the theory and design for "coaches" for computer games, to implement prototypes, and to experiment with their ability to convey important intellectual skills. The focus of this project will be restricted to developing a coach for a single example of an intellectual game called Wumpus. It is pointed out that, while computer games have a powerful educational appeal, they also have a limitation in that the player, on his own, can fail to acquire the skills of an expert. A computer coach, which could provide advice on strategy and tactics for better play and tutor basic mathematical, scientific, or other kinds of knowledge related to the game, could overcome that limitation. The project would address three specific questions: (1) how the expertise can be designed in the coach so that it can respond reasonably to the player's particular choice of move; (2) how the player can be modeled sufficiently so that the coach's remarks are appropriate, i.e., neither too advanced for a beginner nor too elementary for an expert; and (3) how the nature of the coach's advice can be controlled so that it is given in a friendly and personal manner.

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The program analyses carefully the initial situation. It creates some plans and tries to execute them. It analyses the situations deeper in the tree only if the plan fails. In that case it generates new plans correcting what is wrong in the old one. So, the program considers only natural branches of the tree. It can find combinations for which it is necessary to look more than twenty ply ahead. The paper describes the methods used for analyzing a situation and for modifying unsuccessful plans. Then we examine some results found by the program.Artificial Intelligence 8 (1977), 275-321