A classic paper perhaps a bit optimistic about the future, but insightful about the rise of the use of heuristics in the field of problem solving. Imagine how powerful machines can become if we can program them using the heuristics that humans use to solve problems. Simon and Newell speak of machines 'learning' and 'reprogramming' themselves, but what if we think instead of diagnostic information gathering - we could teach the machine how to gather and interpret diagnostic information and steer search and exploration efforts based on the information gathered. Diagnostic information can also lead to the adoption of a resilient position - one that has potential to rebound after an unexpected move or after a point where we have cut-off search efforts in our game tree.
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A. Newell and H. Simon, “Heuristic Problem-Solving: The Next Advance in Operation Research,” Operations Research, Vol. 6, No. 6, 1958.
has been cited by the following article:
TITLE: On the Limit of Machine Intelligence
KEYWORDS: Artificial Intelligence; Nature of Consciousness; Intelligent Computers
JOURNAL NAME: International Journal of Intelligence Science , Vol.3 No.4 , October 23, 2013
ABSTRACT: Whether digital computers can eventually be as intelligent as humans has been a topic of controversy for decades. Neither side of the debate has provided solid arguments proving this way or the other. After reviewing the contentions, we show in this article that machine intelligence is not unlimited. There exists an insurmountable barrier for digital computers to achieve the full range of human intelligence. Particularly, if a robot had a human’s sentience of life and death, then it would cause a logical contradiction. Therefore, a digital computer will never have the full range of human consciousness. This thesis substantiates a limit of computer intelligence and draws a line between biological humans and digital robots. It makes us rethink the issues as whether robots will remain forever one of the tools for us to use, or they will someday become a species competing with us; and whether robots can eventually dominate humans intellectually.
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Tuesday, april 28, 2020, 1957: when machines that think, learn, and create arrived.
[T]he simplest way I can summarize the situation is to say that there are now in the world machines that think, that learn, and that create. Moreover, their ability to do these things is going to increase rapidly until in a visible future--the range of problems they can handle will be coextensive with the range to which the human mind has been applied.
In short, well-structured problems are those that can be formulated explicitly and quantitatively, and that can then be solved by known and feasible computational techniques. ... Problems are ill-structured when they are not well-structured. In some cases, for example, the essential variables are not numerical at all, but symbolic or verbal. An executive who is drafting a sick-leave policy is searching for words, not numbers. Second, there are many important situations in everyday life where the objective function, the goal, is vague and nonquantitative. How, for example, do we evaluate the quality of an educational system or the effectiveness of a public relations department?' Third, there are many practical problems--it would be accurate to say 'most practical problems'--for which computational algorithms simply are not available.
If we face the facts of organizational life, we are forced to admit that the majority of decisions that executives face every day and certainly a majority of the very most important decisions lie much closer to the ill-structured than to the well-structured end of the spectrum. And yet, operations research and management science, for all their solid contributions to management, have not yet made much headway in the area of ill-structured problems. These are still almost exclusively the province of the experienced manager with his 'judgment and intuition.' The basic decisions about the design of organization structures are still made by judgment rather than science; business policy at top-management levels is still more often a matter of hunch than of calculation. Operations research has had more to do with the factory manager and the production-scheduling clerk than it has with the vice-president and the Board of Directors.
Even while operations research is solving well-structured problems, fundamental research is dissolving the mystery of how humans solve ill-structured problems. Moreover, we have begun to learn how to use computers to solve these problems, where we do not have systematic and efficient computational algorithms. And we now know, at least in a limited area, not only how to program computers to perform such problem-solving activities successfully; we know also how to program computers to learn to do these things.
In short, we now have the elements of a theory of heuristic (as contrasted with algorithmic) problem solving; and we can use this theory both to understand human heuristic processes and to simulate such processes with digital computers. Intuition, insight, and learning are no longer exclusive possessions of humans: any large high-speed computer can be programmed to exhibit them also.
I cannot give here the detailed evidence on which these assertions--and very strong assertions they are--are based. I must warn you that examples of successful computer programs for heuristic problem solving are still very few, One pioneering effort was a program written by O.G. Selfridge and G. P. Dinneen that permitted a computer to learn to distinguish between figures representing the letter 0 and figures representing A presented to it 'visually.' The program that has been described most completely in the literature gives a computer the ability to discover proofs for mathematical theorems--not to verify proofs, it should be noted, for a simple algorithm could be devised for that, but to perform the 'creative' and 'intuitive' activities of a scientist seeking the proof of a theorem. The program is also being used to predict the behavior of humans when solving such problems. This program is the product of work carried on jointly at the Carnegie Institute of Technology and the Rand Corporation, by Allen Newell, J. C. Shaw, and myself.
A number of investigations in the same general direction-involving such human activities as language translation, chess playing, engineering design, musical composition, and pattern recognition are under way at other research centers. At least one computer now designs small standard electric motors (from customer specifications to the final design) for a manufacturing concern, one plays a pretty fair game of checkers, and several others know the rudiments of chess. The ILLIAC, at the University of Illinois, composes music, using I believe, the counterpoint of Palestrina; and I am told by a competent judge that the resulting product is aesthetically interesting.
On the basis of these developments, and the speed with which research in this field is progressing, I am willing to make the following predictions, to be realized within the next ten years:
1. That within ten years a digital computer will be the world's chess champion, unless the rules bar it from competition. 2. That within ten years a digital computer will discover and prove an important new mathematical theorem. 3. That within ten years a digital computer will write music that will be accepted by critics as possessing considerable aesthetic value. 4. That within ten years most theories in psychology will take the form of computer programs, or of qualitative statements.
It is not my aim to surprise or shock you if indeed that were possible in an age of nuclear fission and prospective interplanetary travel. But the simplest way I can summarize the situation is to say that there are now in the world machines that think, that learn, and that create. Moreover, their ability to do these things is going to increase rapidly until in a visible future the range of problems they can handle will be coextensive with the range to which the human mind has been applied.