John McCarthy
Abstract
My 1971 Turing Award Lecture was entitled "Generality in Artificial Intelligence." The topic turned out to have been overambitious in that I discovered 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 my previous work rather than attempt something new, but such was not my custom at that time.
I am grateful to ACM for the opportunity to try again. Unfortunately for our science, although perhaps fortunately for this project, the problem of generality in artificial intelligence (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 1987 survey of approaches for achieving generality. Ideas are therefore 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; 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 no one knows how to make a general database of commonsense 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 does not 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 commonsense knowledge are too restricted in their applicability for a general commonsense database. In my opinion, getting a language for expressing general commonsense 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.
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Index Terms
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Generality in artificial intelligence
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John Abel Moyne
This is a belated publication (16 years later) of the ACM A. M. Turing Award Lecture delivered by John McCarthy in 1971 (with revisions and an update). McCarthy says that, at the time that he delivered the lecture, he was unable to put his thoughts on the subject in a satisfactory form. He further says, “The problem of generalizing in artificial intelligence (AI) is almost as unsolved as ever . . . .” and again “It was obvious in 1971 and even in 1958 that AI programs suffered from a lack of generality. It is still obvious; there are many more details.” He believes that the key problem of generality in AI is getting a language for expressing general commonsense knowledge for inclusion in a general database. After discussing various proposals for generalization and showing their problems, McCarthy outlines a proposal for a logical, axiomatic system in which there is heavy reliance on context. The idea is to formalize the notion of context and combine it with the domain restrictive method of nonmonotonic reasoning. Context parameters are added to functions and predicates. Thus, if p is a proposition or sentence in a computer memory, it is to be considered in a particular context ( p, C) where C is the context. This process is further elaborated to allow contexts themselves to have contexts. The author ends the paper by commenting, “All this is unpleasantly vague, but it is a lot more than could be said in 1971.”
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