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Prolog usa a subset of logic (Horn clauses, closely related to “rules” e “production rules”) that permit tractable computation. Rules would mantenha to be influential, providing a foundation for Edward Feigenbaum’a expert systems and the continuing work by Allen Newell and Herbert A. Simon that would lead to Soar and their unified theories of cognition. Critics of the logical approach noted, as Dreyfus had, that human beings rarely used logic when they solved problems. Experiments by psychologists like Peter Wason, Eleanor Rosch, Amos Tversky e Daniel Kahneman and others provided proof. McCarthy, respondei that what people do is irrelevant.
Eu argued that what is really are needed machines that can solve problems—not machines that think as people jo. Among the critics of McCarthy’s approach were his colleagues across the country at MIT. Marvin Minsky, Seymour Papert and Roger Schank am trying to solve problems like “story ” understanding” e “object recognition that required a machine to think like a person. In order to use ordinary concepts like “chair” or “restaurant” they had to make all the same illogical assumptions that people normally made. Unfortunately, imprecise concepts like these are hard to represent in logic.
Schank described their “anti-logic” aproximações as “scruffy”, as opposed to the “puro” paradigms used by McCarthy, Kowalski, Feigenbaum, Newell and Simon. In 1975, in a paper seminal, Minsky noted that many of his fellow “scruffy” researchers were using the same kind of tool: a framework that faças all our common sense assumptions about something.
For example, if we use the concept of a bird, there is a constellation of facts that immediately come to mind: we might assume that it voa, come worms and so on. We know these facts are not always true and that deductions using these facts will not be “logical”, but these structured sets of assumptions are part of the context of everything we say and think. Eu called these structures “frames”. Schank used a version of frames he called “scripts” to successfully answer questions about short stories in English. Many years later object-oriented programming would adopt the idea of essential “inheritance” from AI research on frames.
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In the 1980s a form of AI program called “expert systems” was adopted by corporations in the world and knowledge became the focus of mainstream AI research. In those same years, the Japanese government aggressively funded AI with its fifth generation computer project. Another encorajadores event in the early 1980s was the revival of connectionism in the work of John Hopfield and David Rumelhart. Once again, AI had achieved success.
An expert system is a program that answers questions or solves problems about a specific domain of knowledge, usar o logical rules that are derived from the knowledge of experts. The earliest examples were developed by Edward Feigenbaum and his students.
Dendral, begun in 1965, identified compounds from spectrometer readings. MYCIN, developed in 1972, diagnosed infectious blood diseases. They demonstrated the feasibility of the approach. Expert systems restricted themselves to a small domain of specific base de detalhes de entendimento (thus avoiding the commonsense base de detalhes de entendimento problem) and their claro design made it ” relatively easy for programs to be built and then modified once they were in place. All in all, the programs proved to be useful: something that AI had not been able to achieve up to this point. Em 1980, an expert system called XCON was completed at CMU for the Digital Equipment Corporation.