The Heuristic Programming Project of the Stanford University Computer Science Department is a laboratory of about fifty people whose main goals are to model the nature of scientific reasoning processes in various types of scientific problems and various areas of science and medicine; and to construct expert systems — programs that achieve high levels of performance on tasks that normally require significant human expertise for their solution.

"The contract net protocol has been developed to specify problem-solving communication and control for nodes in a distributed problem solver. Task distribution is affected by a negotiation process, a discussion carried on between nodes with tasks to be executed and nodes that may be able to execute those tasks. We present the specification of the protocol and demonstrate its use in the solution of a problem in distributed sensing. The utility of negotiation as an interaction mechanism is discussed. It can be used to achieve different goals, such as distributing control and data to avoid bottlenecks and enabling a finer degree of control in making resource allocation and focus decisions than is possible with traditional mechanisms." IEEE Transactions on Computers C-29(12):1104-1113. PDF: http://www.reidgsmith.com/The_Contract_Net_Protocol_Dec-1980.pdf.

"In the fall of 1978 we decided to produce a special issue of the SIGART Newsletter devoted to a survey of current knowledge representation research. We felt that there were twe useful functions such an issue could serve. First, we hoped to elicit a clear picture of how people working in this subdiscipline understand knowledge representation research, to illuminate the issues on which current research is focused, and to catalogue what approaches and techniques are currently being developed. Second -- and this is why we envisaged the issue as a survey of many different groups and projects -- we wanted to provide a document that would enable the reader to acquire at least an approximate sense of how each of the many different research endeavours around the world fit into the field as a whole. It would of course be impossible to produce a final or definitive document accomplishing these goals: rather, we hoped that this survey could initiate a continuing dialogue on issues in representation, a project for which this newsletter seems the ideal forum. It has been many months since our original decision was made, but we are finally able to present the results of that survey. Perhaps more than anything else, it has emerged as a testament to an astounding range and variety of opinions held by many different people in many different places. The following few pages are intended as an introduction to the survey as a whole, and to this issue of the newsletter. We will briefly summarize the form that the survey took, discuss the strategies we followed in analyzing and tabulating responses, briefly review the overall sense we received from the answers that were submitted, and discuss various criticisms which were submitted along with the responses. The remainder of the volume has been designed to be roughly self-explanatory at each point, so that one may dip into it at different places at will. Certain conventions, however, particularly regarding indexing and tabulating, will also be explained in the remainder of this introduction." ACM SIGART Newsletter No. 70.

ACM Computing Survey 12:213-253

Earlier version in IJCAI 6 (1979). Artificial Intelligence, 14 (2), 165-203.

This work is an attempt to formally represent the knowledge required for the solution of a difficult retrograde chess problem (figure I). This solution Includes the extension of a formal deductive system to Include an observational facility. FOL [9], we have detailed a proof of the solution of the puzzle, Including proofs for almost all of the necessary associated lemmas [2], We shall highlight the various representational decisions made In the process of axiomatiiing retrograde chess, discussing both the necessity for these particular choices, and their Implications for designers of representations for other domains. This work is part of the search for epistemologically effective formalisms for artificial Intelligence.

This approach leads to an infinite sequence of algorithms I. Surface Models

I will remain Ion8 and deeply indebted to Herb Simon, H. T. Kung, AI Newell, Jay

IJCAI 6, 1-3

Our purpose in studying natural language understanding in conjunction with problem solving is to bring together the constraints of what formal representation can actually be obtained with the question of what knowledge is required in order to solve a wide range of problems in a semantically rich domain. We believe that these issues cannot sensibly be tackled in isolation. In practical terms we have had the benefits of an increased awareness of common problems in both areas and a realisation that some of our techniques are applicable to both the control of inference and the control of parsing. Early work on solving mathematical problems stated in natural language was done by Bobrow (STUDENT - (i]) and Chamiak (CARPS - [5]). However the rudimentary parsing and simple semantic structures used by Bobrow and Charniak are inadequate for any but the easiest problems. Our intention has been to build on B/RG Chris This work was supported by SRC grant number 94493 and an SRC research studentship for Mellish.