Newell, A., 1969. Heuristic programming: ill-structured problems, Progress in Operations Research, Vol. III (Aronofsky, ed.), 360-414.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....dot product definition of linear algebra, is an instance of Allan Newell s and Edward Feigenbaum s insight of the fundamental tradeoff between a problem solvers generality and its expertness. Newell states: there is an inverse relationship between the generality of a method and its power [36] (page 12) Feigenbaum identifies a law of nature operating that relates problem solving generality (breadth of applicability) inversely to power (solution success. and power directly to specificity (task specific information) 22] page 6) These principles are observed in the grid generation ....

A. Newell. Heuristic Programming: ILL-Structured Problems. In P.S. Rosenbloom, J. E. Laird, and A. Newell, editors, The Soar Papers: Research on Integrated Intelligence (Volume 1), pages 3-54. MIT Press, London, 1993.

.... main goal of the study was to create an evaluation method for measuring performance of Boolean queries across a wide operational range by elaborating the ideas introduced by Harter [10] The method is presented and argued using the framework suggested by Newell M= domain, procedure, justification [19]: 1. The domain of the method specifies the appropriate application area for the method. 2. The procedure of the method consists of the ordered set of operations required in the proper use of the method. Especially, two major operations unique to the procedure need to be elaborated: a) Query ....

Newell, A. (1968). Heuristic programming: Ill-structured problems. In: Arofonsky, J. (Ed.). Progress in Operations Research, Vol III, 360-414. New York.

....specialize code that is locally general but, due to global properties (e.g. invariant expressions) can be specialized. Another example is found in the AI literature on problem solving methods: the socalled weak problem solving methods work less efficiently than the so called strong methods [443]. The difference between strong and weak methods is essentially that strong methods take the specifics of the problem into account they are specialists. This same principle of specialization is commonly found in cognitive science and HCI. In cognitive science one can find models of specialized ....

....but similar concepts are known variously as tactics, heuristic strategies, and task strategies. In other areas of computing, similar concepts are known as algorithmic skeletons [143] The PSM work is a descendant of work on generic PSMs: the so called weak methods such as means ends analysis [443]. The weak methods are entirely problem independent, and although in principle they should always work, they do not take advantage of particular details of the task or environment in order to be efficient. Generally speaking, the more knowledge one has about the task and task environment, the ....

Newell, A. Heuristic programming: Ill-structured problems. In Progress in Operations Research: Relationship Between Operations Research and the Computer, J. Aronofsky, Ed., vol. 3. John Wiley & Sons, New York, 1969, pp. 360--413.

....may be a special case of their general unintelligence: They may simply not be smart enough to know what to do when a limited stock of methods fails to apply. But this needn t be a principled limitation of Classical architectures: There is, to our knowledge, no reason to believe that something like Newell s (1969) hierarchy of weak methods or Laird, Rosenberg and Newell s (1986) universal subgoaling, i s n principle incapable of dealing with the problem of graceful degradation. Nor, to our knowledge, has any argument yet been offered that Connectionist architectures are in principle capable of ....

Newell, A. (1969). Heuristic programming: Ill-structured problems. In Aronofsky, J. (ed.) Progress in Operations Research, III, New York, John Wiley & Sons.

....to solve a different problem. If the generator is generating a sufficiently large set of candidates, we can simply change the test to select different solutions. However, after test incorporation, the separation between generator and test is lost, and the problem solver is harder to modify (see Newell, 1969). Finally, test incorporation is knowledge aligned. Test incorporation can be viewed as the transfer of knowledge out of the test and into the generator. Hence, test incorporation is a fundamental way in which knowledge comes to be effectively applied in problem solving. The purpose of this ....

....and the vast array of expert systems that have followed it have demonstrated that it is possible to engineer these highly efficient and specialized methods for a wide variety of domains. Let us consider the theories of intelligence that are implicit in the methodology of expert systems and in Newell s (1969) paper. The work on expert systems describes intelligent systems as possessing a large collection of highly specialized methods, each applicable to a different task. This big switch theory of intelligence says that a system is more intelligent if it has more of these specialized methods. ....

Newell, A. 1969. Heuristic programming: ill-structured problems, in Progress in Operations Research, Arnofsky, J., (ed.), New York: Wiley. 363--414.

....such as means ends analysis, generate and test, hypothesize and match, and hill climbing. He hypothesized that these types of methods, which were used when there was little task6 specific knowledge available, were not just a random collection, but instead a family, which he termed weak methods [ Newell, 1969 ] From here on in we ll refer to this as the Weak Methods Hypothesis, though Newell did not explicitly so name it) The weak methods were weak, not because they failed to tightly constrain the search, but because they made weak demands on the knowledge about the task required to apply the ....

A. Newell. Heuristic programming: Ill-structured problems. In J. Aronofsky, editor, Progress in Operations Research, III, pages 360--414. Wiley, New York, 1969.

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Newell, A., 1969. Heuristic programming: ill-structured problems, Progress in Operations Research, Vol. III (Aronofsky, ed.), 360-414.

No context found.

A. Newell, "Heuristic Programming: Ill-structured problems," In Progress in Operations Research, III, J. Aronofsky, editor, Wiley, New York, 1969.

No context found.

Newell, A. 1969. Heuristic programming: ill-structured problems, in Progress in Operations Research, Arnofsky, J., (ed.), New York: Wiley. 363--414.

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