Explores the present and future uses and capabilities of computers and examines computer hardware and software.

For many years much of the research frontier of computer science as represented, for example, by the activities of artificial intelligence laboratories, seemed preoccupied with esoteric mathematical studies (such as self organizing systems, algebraic theory of machines, or resolution theorem proving), "toy" systems (such as games, puzzles, children's blocks), or far-out science fiction goals (such as robots for space exploration). Now many of these same laboratories are applying the techniques they have developed in the past to important, short term, real world tasks---and uncovering significant new research problems in the process. By working in new interdisciplinary teams, the computer scientists and the applications specialists have begun to develop an evolving series of novel systems whose potential value to our society is tremendous.

article Free Access Share on New Programming Languages for Artificial Intelligence Research Authors: Daniel G. Bobrow Xerox Palo Alto Research Center, Palo Alto, California Xerox Palo Alto Research Center, Palo Alto, CaliforniaView Profile , Bertram Raphael Stanford Research Institute, Menlo Park, California Stanford Research Institute, Menlo Park, CaliforniaView Profile Authors Info & Claims ACM Computing SurveysVolume 6Issue 3Sept. 1974 pp 153–174https://doi.org/10.1145/356631.356632Online:01 September 1974Publication History 155citation2,143DownloadsMetricsTotal Citations155Total Downloads2,143Last 12 Months299Last 6 weeks86 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

There are numerous indications that the field of Artificial intelligence (AI) is now well established: a specialized journal is entering its third year of publication; the third major biennial conference organized by an ad hoc international council will be held in August; several textbooks and paper collections have been published, and several Computer Science Departments have AI specialties; and some important disciplines whose roots are in AI, such as Pattern Recognition and Symbolic Algebraic Manipulation, have already spun off as independent areas. Still, when I identify my field as Artificial intelligence, I frequently am met by blank stares, snickers, or questions concerning whether that means building simulated rats or planting false military rumors. I hope this issue of COMPUTER will clear up some of the confusion.

Our paper on the use of heuristic information in graph searching defined a path-finding algorithm, A*, and proved that it had two important properties. In the notation of the paper, we proved that if the heuristic function ñ (n) is a lower bound on the true minimal cost from node n to a goal node, then A* is admissible; i.e., it would find a minimal cost path if any path to a goal node existed. Further, we proved that if the heuristic function also satisfied something called the consistency assumption, then A* was optimal; i.e., it expanded no more nodes than any other admissible algorithm A no more informed than A*. These results were summarized in a book by one of us.

This paper introduces a new notation, called “resolution graphs”, for deductions by resolution in first-order predicate calculus. A resolution graph consists of groups of nodes that represent initial clauses of a deduction and links that represent unifying substitutions. Each such graph uniquely represents a resultant clause that can be deduced by certain alternative but equivalent sequences of resolution and factoring operations.

Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.

SIR is a computer system, programmed in the LISP language, which accepts information and answers questions expressed in a restricted form of English. This system demonstrates what can reasonably be called an ability to "understand" semantic information. SIR''s semantic and deductive ability is based on the construction of an internal model, which uses word associations and property lists, for the relational information normally conveyed in conversational statements. A format-matching procedure extracts semantic content from English sentences. If an input sentence is declarative, the system adds appropriate information to the model. If an input sentence is a question, the system searches the model until it either finds the answer or determines why it cannot find the answer. In all cases SIR reports its conclusions. The system has some capacity to recognize exceptions to general rules, resolve certain semantic ambiguities, and modify its model structure in order to save computer memory space. Judging from its conversational ability, SIR is more "intelligent" than any existing question-answering system. The author describes how this ability was developed and how the basic features of SIR compare with those of other systems. The working system, SIR , is a first step toward intelligent machine communication. The author proposes a next step by describing how to construct a more general system which is less complex and yet more powerful than SIR . This proposed system contains a generalized version of the SIR model, a formal logical system called SIR1 , and a computer program for testing the truth of SIR1 statements with respect to the generalized model by using partial proof procedures in the predicate calculus. The thesis also describes the formal properties of SIR1 and how they relate to the logical structure of SIR .

For the purpose of this paper, a question-answering system is a computer program that has at least the following three characteristics: (1) The ability to accept statements of fact and store them in its memory (2) The ability to search stored information efficiently and to recognize items that are relevant to a particular query (3) The ability to respond appropriately to a question by identifying and presenting the answer if it is present in memory, and by deducing a reasonable logical response from relevant knowledge if the complete answer is not explictly available.

: This report describes progress toward an 'intelligent question- answering system'--a system that can accept facts, retrieve items from memory, and perform logical deductions necessary to answer questions. Two versions of such a system have been implemented, and the authors expect these to be the first in an evolving series of question-answerers. The first system, QA1, is based upon relational information organized in a list-structured memory. The data consist of general facts about relations as well as specific facts about objects. QA1 has limited deductive ability. QA2 is based upon formal theorem- proving techniques. Facts are represented by statements in the predicate calculus. Although the memory organization is simpler than that of QA1, the sophisticated logical abilities of QA2 result in greater question-answering power. The report gives examples of the performance of QA1 and QA2 on typical problems that have been done by previous question-answerers, and describes plans for extending the capabilities of QA2.