Generality in artificial intelligence

  title={Generality in artificial intelligence},
  author={John McCarthy},
  journal={Commun. ACM},
  • J. McCarthy
  • Published 1 December 1987
  • Computer Science
  • Commun. ACM
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… 
Artificial Intelligence: From programs to solvers
The problem of generality in AI is revisited, the way in which this ’Models and Solvers’ agenda addresses the problem is looked at, and the relevance of this agenda to the grand AI goal of a computational account of intelligence and human cognition is discussed.
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The purpose of this paper is to show that propositional contextual reasoning is decidable.
What Computers Still Can't Do
General intelligence disentangled via a generality metric for natural and artificial intelligence
This work dissects the notion of general intelligence into two non-populational measures, generality and capability, which it applies to individuals and groups of humans, other animals and AI systems, and relates the individual measure of generality to traditional notions of general Intelligence and cognitive efficiency in humans, collectives, non-human animals and machines.
Making Robots Conscious of Their Mental States
Thinking about consciousness with a view to designing it provides a new approach to some of the problems of consciousness studied by philosophers and one advantage is that it focusses on the aspects of consciousness important for intelligent behavior.
Comparing formal theories of context in AI
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A new formal definition of intelligence is articulate, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience, and a set of guidelines for what a general AI benchmark should look like are proposed.
Behavioural artificial intelligence: an agenda for systematic empirical studies of artificial inference
It is argued that due to the complexity and opacity of artificial inference, one needs to initiate systematic empirical studies of artificial intelligent behavior similar to what has previously been done to study human cognition, judgment and decision making, to provide valid knowledge about the judgments and decisions made by intelligent systems.
A Framework for Searching for General Artificial Intelligence
This document seeks to describe and unify principles that guide the basis of the development of general artificial intelligence, and defines intelligence as the ability to acquire skills that narrow this search, diversify it and help steer it to more promising areas.
Contextual Reasoning
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Some Expert Systems Need Common Sense
  • J. McCarthy
  • Medicine
    Annals of the New York Academy of Sciences
  • 1984
The object of this lecture is to describe common sense abilities and the problems that require them in an expert system, a program for advising physicians on treating bacterial infections of the blood and meningitis.
A Logic for Default Reasoning
  • R. Reiter
  • Philosophy, Computer Science
    Artif. Intell.
  • 1980
Applications of Circumscription to Formalizing Common Sense Knowledge
Non-Monotonic Logic I
First Order Theories of Individual Concepts and Propositions.
We discuss rst order theories in which individual concepts are admitted as mathematical objects along with the things that reify them. This allows very straightforward formalizations of knowledge,
A Learning Machine: Part II
An effort is made to improve the performance of the learning machine described in Part I, and the over-all effect of various changes is considered. Comparative runs by machines without the scoring
Truth Maintenance Systems for Problem Solving
It is shown that reasoning programs which take care to record the logical justifications for program beliefs can apply several powerful, but simple, domain-independent algorithms to maintain the consistency of program beliefs and realize substantial search efficiencies.