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Logic in Cognitive Science: Bridging the Gap between Symbolic and
Logic in Cognitive Science: Bridging the Gap between Symbolic and

... (2) D; (3) D, 3, and 7; (4) D and 7. The classically correct answer ranks fourth, while an instance of affirming the consequent (i.e. judging that 3 is relevant for determining if the rule is correct) ranks first. Wason’s robust and easily reproducible results seem to show that most people are poor ...
CIS 730 (Introduction to Artificial Intelligence) Lecture
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Decision Support and Expert Systems (24)

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Social Robots: Approaches and Conceptions in the Perspective of
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Advances in Artificial/Computational Intelligence and Neuroscience
Advances in Artificial/Computational Intelligence and Neuroscience

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What is Artificial Intelligence? Psychometric AI as an Answer
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Modeling and Forecasting the Information Sciences

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... intelligent programs to perform the complicated task. In 1950s, Alan Turing presented a paper on Computing Machinery and Intelligence. The result of this paper was if a machine could pass certain test (known as Turing test) then it could be intelligent. In this paper Turing also considered a number ...
Multi-Agent Systems - AI-MAS
Multi-Agent Systems - AI-MAS

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... how these different types of knowledge relate to one another and how they will function together in the overall context of the intelligent system. Once knowledge is encoded, it must be entered manually by keyboarding. The time investment to determine, represent, and enter knowledge can be significan ...
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Dialogue systems: simulations or interfaces?
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... • So, perhaps we can do real AI, provided we can build robot infants that are raised by parents and socialised into society by human beings who treat them as equals – This probably requires people to actually think that these AI systems are human – These systems will have the same ethical status as ...
Intelligent Library Systems: Artificial Intelligence Technology and
Intelligent Library Systems: Artificial Intelligence Technology and

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EXPERT SYSTEMS - THE NEW BUSINESS SIMULATION TOOL
EXPERT SYSTEMS - THE NEW BUSINESS SIMULATION TOOL

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Investigate the Effect of Expert Systems Application on Management

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Software Agents, Multi-Agent Systems, and Data Mining
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History of artificial intelligence

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with ""an ancient wish to forge the gods.""The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true. Eventually it became obvious that they had grossly underestimated the difficulty of the project. In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again. This cycle of boom and bust, of ""AI winters"" and summers, continues to haunt the field. Undaunted, there are those who make extraordinary predictions even now.Progress in AI has continued, despite the rise and fall of its reputation in the eyes of government bureaucrats and venture capitalists. Problems that had begun to seem impossible in 1970 have been solved and the solutions are now used in successful commercial products. However, no machine has been built with a human level of intelligence, contrary to the optimistic predictions of the first generation of AI researchers. ""We can only see a short distance ahead,"" admitted Alan Turing, in a famous 1950 paper that catalyzed the modern search for machines that think. ""But,"" he added, ""we can see much that must be done.""
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