In H. O'Neil (Ed.), Procedures for instructional systems development. New York: Academic Press, 273-313.

... we began to program a computer understanding system thatwould attempt to process input texts. An item crucial to our ability to accomplishthis task was what we called a script. A script is a frequently repeated causalchain of events that describes a standard situation. In understanding, when it ispossible to notice that one of these standard event chains has been initiated,then it is possible to understand predictively. That is, if we know we are in arestaurant then we can understand where an "order" fits with what we justheard, who might be ordering what from whom, what preconditions (menu,sitting down) might have preceded the "order", and what is likely to happennext. All this information comes from the restaurant script.Hayes, J.E., D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9, Ellis Horwood.

Proceedings of the Sixth International Joint Conference on Artificial Intelligence, Cambridge, Mass., 645-655

In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 13). New York: Academic Press, 277-318

New York: Elsevier-North Holland

To choose their actions, reasoning programs must be able to make assumptions and subsequently revise their beliefs when discoveries contradict these assumptions. The Truth Maintenance System (TMS) is a problem solver subsystem for performing these functions by recording and maintaining the reasons for program beliefs. Such recorded reasons are useful in constructing explanations of program actions and in guiding the course of action of a problem solver. This paper describes (1) the representations and structure of the TMS, (2) the mechanisms used to revise the current set of beliefs, (3) how dependency-directed backtracking changes the current set of assumptions, (4) techniques for summarizing explanations of beliefs, (5) how to organize problem solvers into "dialectically arguing" modules, (6) how to revise models of the belief systems of others, and (7) methods for embedding control structures in patterns of assumptions. We stress the need of problem solvers to choose between alternative systems of beliefs, and outline a mechanism by which a problem solver can employ rules guiding choices of what to believe, what to want, and what to do.Artificial Intelligence 12(3):231-272

This report describes a knowledge-base system in which the information is stored in a network of small parallel processing elements??de and link units??ich are controlled by an external serial computer. This network is similar to the semantic network system of Quillian, but is much more tightly controlled. Such a network can perform certain critical deductions and searches very quickly; it avoids many of the problems of current systems, which must use complex heuristics to limit and guided their searches. It is argued (with examples) that the key operation in a knowledge-base system is the intersection of large explicit and semi-explicit sets. The parallel network system does this in a small, essentially constant number of cycles; a serial machine takes time proportional to the size of the sets, except in special cases.

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

In J. E. Hayes, D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9. Chichester, England: Ellis Horwood Ltd., 149-195

Cognitive Science, vol. 3, No. 1, pp. 67-90.