John McCarthy
Computer Science Department
Stanford University
Stanford, CA 94305
jmc@cs.stanford.edu
http://www-formal.stanford.edu/jmc/
1971-1987
My 1971 Turing Award Lecture was entitled ``Generality in Artificial Intelligence''. The topic turned out to have been overambitious in that I discovered that I was unable to put my thoughts on the subject in a satisfactory written form at that time. It would have been better to have reviewed previous work rather than attempt something new, but such wasn't my custom at that time.
I am grateful to the ACM for the opportunity to try again. Unfortunately for our science, although perhaps fortunately for this project, the problem of generality in AI is almost as unsolved as ever, although we now have many ideas not available in 1971. This paper relies heavily on such ideas, but it is far from a full 1986 survey of approaches for achieving generality. Ideas are discussed at a length proportional to my familiarity with them rather than according to some objective criterion.
It was obvious in 1971 and even in 1958 that AI programs suffered from a lack of generality. It is still obvious, and now there are many more details. The first gross symptom is that a small addition to the idea of a program often involves a complete rewrite beginning with the data structures. Some progress has been made in modularizing data structures, but small modifications of the search strategies are even less likely to be accomplished without rewriting.
Another symptom is that no-one knows how to make a general database of common sense knowledge that could be used by any program that needed the knowledge. Along with other information, such a database would contain what a robot would need to know about the effects of moving objects around, what a person can be expected to know about his family, and the facts about buying and selling. This doesn't depend on whether the knowledge is to be expressed in a logical language or in some other formalism. When we take the logic approach to AI, lack of generality shows up in that the axioms we devise to express common sense knowledge are too restricted in their applicability for a general common sense database. In my opinion, getting a language for expressing general common sense knowledge for inclusion in a general database is the key problem of generality in AI.
Here are some ideas for achieving generality proposed both before and after 1971. I repeat my disclaimer of comprehensiveness.