A program called "AM" is described which carries on simple mathematics research,defining and studying new concepts under the guidance of a large body ofheuristic rules. The 250 heuristics communicate via an agenda mechanism, aglobal priority queue of small tasks for the program to perform, and reasons whyeach task is plausible (for example, "Find generalizations of 'primes', because'primes' turned out to be so useful a concept"). Each concept is represented asan active, structured knowledge module. One hundred very incomplete modulesare initially supplied, each one corresponding to an elementary set-theoreticconcept (for example, union). This provides a definite but immense space whichAM begins to explore. In one hour, AM rediscovers hundreds of common concepts(including singleton sets, natural numbers, arithmetic) and theorems (for example,unique factorization).Summary of Ph.D. dissertation.Hayes, J.E., D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9, Ellis Horwood.

Cognitive Science 3(3):177-212.

CACM, 22 (10), 560-70.

Artificial Intelligence, 12 (1), 23-40.

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