John Wiley and Sons, Inc. Robinson, J.A. (1965) "A Machine Oriented Logic Based on the Resolution Principle".

CACM 18:107-115.

New York: Academic Press

Communication of the ACM 18(5):264-274, (reprinted in RNLP: 193-204)

IJCAI 4, 134-142.

See also Werbos, Paul J. (1994). The Roots of Backpropagation. From Ordered Derivatives to Neural Networks and Political Forecasting. New York, NY: John Wiley & Sons, Inc. Rumelhart, David E.; Hinton, Geoffrey E., Williams, Ronald J. (8 October 1986). "Learning representations by back-propagating errors". Nature323 (6088): 533–536. doi:10.1038/323533a0.Ph.D. thesis, Harvard University.

In L. W. Gregg (Ed.), Knowledge and cognition. Hillsdale, NJ: Erlbaum

"New directions in Artificial Intelligence research have led to the need for certain novel features to be embedded in programming languages. This paper gives an overview of the nature of these features, and their implementation in four principal families of AI languages: SAIL; PLANNER/CONNIVER; QLISP/INTERLISP; and POPLER/POP-2. The programming featurcs described include: new data types and accessing mechanisms for stored expressions; more flexible control structures, including multiple processes and backtracking; pattern matching to allow comparison of data item with a template, and extraction of labeled subexpressions; and deductive mechanisms which allow the programming system to carry out certain activities including modifying the data base and deciding which subroutines to run next using only constraints and guidelines set up by the programmer." ACM Computing Surveys, Vol. 6, No. 3, September 1974, pp. 155 174.

AI Memo 237, AI Laboratory, Stanford University. (Also in A. Zampolli, Ed., Linguistic structures processing. Amsterdam: North-Holland,

Artificial Intelligence 5:137-148.