This paper proposes a method for handling the frame problem in representing conceptual, or natural-language-type information. The method is part of a larger calculus for expressing conceptual information, called P c F-2, which is described in Sandewall (1972), and which is a modification and extension of Sandewall (1971a). When the STRIPS schema adds a fact, PLANNER would add the corresponding fact to the data base using the primitive thassert. In this context, by epistemological information we mean a notation together with a set of rules (for example, logical axioms) which describe permissible deductions.
Sociologists are concerned to predict the effect of changes on future society.But is prediction in principle possible when intelligence is involved? Ifintelligence is the production of novelty, accurate prediction might seem to bestrictly impossible. However this may be, it seems that the present troubleabout social prediction is simply that there are no adequate theoreticalmodels of societies. This means that politicians are almost powerless topredict, plan, or control, except with incredible errors. We find ourselves injust this position in trying to assess the implications of future intelligence.Machine Intelligence 6
A general game-playing program must know the rules of the particular playing game. These rules are:(1) an algorithm indicating the winning state;(2) an algorithm enumerating legal moves. A move gives a set of changes from the present situation.There are two means of giving these rules:(1) We can write a subroutine which recognizes if we have won and another which enumerates legal moves. Such a subroutine is a black box giving to the calling program the answer: 'you win' or 'you do not win', or the list of legal moves. But it cannot know what is in that subroutine.(2) We can also define a language in which we describe the rules of a game. The program investigates the rules written with this language and finds some indications to improve its play. Artificial Intelligence and Heuristic Programming Edinburgh University Press
We may regard the subject of artificial intelligence as beginning with Turing's article'Computing Machinery and Intelligence' (Turing 1950) and with Shannon's (1950) discussion of how a machine might be programmed to play chess. In this case we have to say that a machine is intelligent if it solves certain classes of problems requiring intelligence in humans, or survives in an intellectually demanding environment. However, we regard the construction of intelligent machines as fact manipulators as being the best bet both for constructing artificial intelligence and understanding natural intelligence. Given this notion of intelligence the following kinds of problems arise in constructing the epistemological part of an artificial intelligence: I.
Attempts to write'intelligent' computer programs have commonly involved the choice for attack of some particular aspect of intelligent behaviour, together with the choice of some relevant task, or range of tasks, which the program must perform. Toda (1962), in a whimsical and illuminating paper, has discussed the problems facing an automaton in a simple artificial environment. The reader may find it illuminating to imagine himself (the automaton) before a screen on which is displayed a complex pattern which changes from time to time (sequence of states). These are: 1. the subjective environment graph (figure 1), 2. the stored graph which is that portion of the subjective environment graph which the automaton has stored in its memory as a result of its experience (figure 2 (b)), and 3. the option graph which is that fragment of the stored graph which the automaton'knows' how to reach (figure 2(c)).
While still unable to outplay checker masters, the program's playing ability has been greatly improved. Limited progress has been made in the development of an improved book-learning technique and in the optimization of playing strategies as applied to the checker playing program described in an earlier paper with this same title.' While the investigation of the learning procedures forms the essential core of the experimental work, certain improvements have been made in playing techniques which must first be described. The way in which two limiting values (McCarthy's alpha and beta) are used in pruning can be seen by referring The move tree of Figure 1 redrawn to illustrate the detailed method used to keep track of the comparison values.
"A formal theory is given concerning situations, causality and the possibility and effects of actions is given. The theory is intended to be used by the Advice Taker, a computer program that is to decide what to do by reasoning. Some simple examples are given of descriptions of situations and deductions that certain goals can be achieved."Reprinted in M. Minsky (ed.), Semantic Information Processing, Cambridge, MA: MIT Press, 1968. Related topics are explored in J. McCarthy and Patrick Hayes, Some Philosophical Ideas From the Standpoint of Artificial Intelligence," MI-4, 1969.Stanford Artificial Intelligence Project Memo No 2, July 1963
... The literature does not include any general discussion of the outstanding problems of this field. In this article, an attempt will be made to separate out, analyze, and find the relations between some of these problems. Analysis will be supported with enough examples from the literature to serve the introductory function of a review article, but there remains much relevant work not described here.Proc. Institute of Radio Engineers 49, p. 8-30
Mr. J. H. H. Merriman was educated at King's College School, Wimbledon, and King's College, University of London. in 1935 and did Postgraduate Research at King's College London obtaining his M.Sc. We are, therefore, likely to see in the immediate future a movement away from the concept of single purpose automatic data processing installations to installations or systems of installations which, in the first placeT, will be multi--purpose and, in due course, integrated. If we are, therefore, to imagine large complex multipurpose integrated data processing system, we must imagine them to be serviced, to an increasing extent, by separate installations which will analyse the operations of the integrated system, determine the most appropriate operating conditions and which will, to some extent, relieve the burden of programming by automatic access to inbuilt programming routines.
The studies reported here have been concerned with the programming of a digital computer to behave in a way which, if done by human beings oranimals, would be described as involving the process of learning. Whilethis is not the place to dwell on the importance of machine-learning procedures,or to discourse on the philosophical aspects,1 there is obviously avery large amount of work, now done by people, which is quite trivial inits demands on the intellect but does, nevertheless, involve some learning.Also in Computers and Thought. Feigenbaum, Edward A. and Julian Feldman (Editors) 1963.See also:IEEE XploreSome Studies in Machine Learning Using the Game of Checkers, II - Recent ProgressIBM Journal of Research and Development, 3:211-229