Reprinted as Chapter 3 (pp.250-271) of Webber & Nilsson, Readings in Artificial IntelligenceElcock, E. W. and Michie, D. (Eds.), Machine Intelligence 8, pp. 94-€“138. Ellis Horwood.

Earlier work by Newell, Simon, Shaw, and Gelernter in the middle and late 1950s emphasized the heuristic approach, but the weight soon shifted to various syntactic methods culminating in a large effort on resolution type systems in the last half of the 1960s. It was about 1970 when considerable interest was revived in heuristic methods and the use of human supplied, domain dependent, knowledge. It is not my intention here to slight the great names in automatic theorem proving, and their contributions to all we do, but rather to show another side of it. For recent books on automatic theorem proving see Chang and Lee [19], Loveland [44], and Hayes [31]. Also see Nilsson's recent review article [61]. The word "resolution" has come to be associated with general purpose types of theorem provers which use very little domain dependent information and few if any special heuristics besides those of a syntactic nature. It has also connoted the use of clauses and refutation proofs. There was much hope in the late 60's that such systems, especially with various exciting improvements, such as set of support, model elimination, etc., would be powerful provers. But by the early 70's there was emerging a belief that resolution type systems could never really "hack" it, could not prove really hard mathematical theorems, without some extensive changes in philosophy.

Austin Tate Department of Artificial Intelligence University of Edinburgh Edinburgh Scotland Abstract Procedures for optimization and resource allocation in Operations Research first require a project network for the task to be specified. The specification of a project network is at present done in an intuitive way. AI work in plan formation has developed formalisms for specifying primitive activities, and recent work by Sacerdoti (1975a) has developed a planner able to generate a plan as a partially ordered network of actions. The "planning: a joint AI/OR approach" project at Edinburgh has extended such work and provided a hierarchic planner which can aid in the generation of project networks. This paper describes the planner (NONLIN) and the Task Formalism (TF) used to hierarchically specify a domain. Current work in Operations Research (OR) and Artificial Intelligence (AI) has concentrated on different aspects of the problem. We have taken an interdisciplinary approach in the hope that this will lead to a development of both these aspects. In the OR approach, the planning process falls into two stages. The constituent "jobs" of a plan are specified together with their precedence relationships (i.e.

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We describe work on a project aimed at producing an interactive program for the construction of project networks (e.g. for house building tasks). To do this we have developed a planner which can form plans epresented as a partiQlly ordered netwo k of actions. A formalism (TF) is given for describing a domain in a hierarchic fashion. The representation of plans and the planner (NONLIN) are fully explained. During this work, a general technique was developed for answering queries about Q situation when the informQtion about the world is stored as a partiQlly ordered network of alterations made to some initial situation. We give a general procedure for recognizing and correcting for interactions between actions in the network. This is based on an analysis of the goal structure of the problem. The work is compared to that of Sacerdoti (l975a) who pioneered the techniques of planning using plans represented as partially ordered networks of actions.

Boitet, C. 1977 Where Does GETA Stand at the Beginning of Brinkmann, K.-H.

Research in computer chess has been active for over three decades. Over that period, computer chess has fallen from the position of being a prominent research application in artificial intelligence to a peripheral area. In this paper, we take a retrospective look at what has been accomplished, in order to understand where the field is today and where it is headed tomorrow. Whereas the past has often been clouded by engineering passing as science, misspent effort for short-term gains, and research results with little applicability to other domains, there is evidence that computer chess is emerging from the shadow of its past and may now be recapturing some of its lost stature in the research world.

Northwestern students who are interested in changing their major, or simply sampling the computer science field, can take individual introductory courses.

At the present stage of research in artificial intelligence , machines are stil l remote from achieving a level of intelligence comparable in complexity to human thought. As computer applications become more sophisticated, however, and thus more influential in human affairs , it becomes increasingly important to understand both the capabilities and limitations of machine Intelligence and its potential impact on society. To this end, the artificial intelligence field was ex­amined in a systematic manner. The study was divided into two parts : (1) Delineation of areas of artificial intelligence, and postulatio " of hypothetical prod­ucts resulting from progress in the field , and (2) A judgmental portion, which involved appli­cations and implications of the products to society . For the latter purpose, a Delphi study was conducted among experts in the artificial intelligence field to solicit their opinion concerning prototype and com­mercial dates for the products, and the possibility and desirability of their applications and implications .In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.

In one major section we discuss the imprecision, the incompleteness, the openendedness, and the uncertainty of people's knowledge. In the other major section we discuss strategies people use to make different types of deductive, negative, and functional inferences, and the way uncertainties combine in these inferences. Keywords Semantics, inference, cognitive processes, natural language processing, human memory, question-answering systems, deduction, analogy 1. Introduction In this paper we will discuss how to represent and process information in a computer in ways that are natural to people. This does not mean doing away completely with representations and procedures which computers have traditionally used, but adding new representations and procedures which they have not used. People often store and communicate imprecise, incomplete, and unquantified information; they often assert truth or falsity in relative terms; and they seldom seem to use rigorous logic in their inferential processes. Because of these conditions, people seem to have an almost infinite information processing capacity, with inference making and problem solving abilities more refined and far more flexible than any existing computer program. How can we study these human capabilities in order to make our machines show similar performance? A combination of approaches is perhaps best. Observation of people's behavior, introspection, some experimentation, protocol analysis, and synthesis of computer programs can all be valuable techniques.