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hierarchical knowledge-based process planning in
hierarchical knowledge-based process planning in

... costs, larger flexibility, improved quality and higher productivity. Over the last three decades vast efforts have been made in developing novel methods and architectures for CAPP systems. The last twenty years of research has been dominated by the application of artificial intelligence (AI) methods ...
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... problem-solving tasks (Engelbart, 1962). Through the work of Engelbart’s Augmentation Research Center, and other groups in the 1950s and 1960s, many of the devices we take for granted today were invented as “augmentation” tools including: the mouse, interactive graphical displays, keyboards, trackba ...
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... General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). It is an art performed by human designers to extract the r ...
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... game. The shapes are meant to be diverse and pose different obstacles. Closed and cross allow player configurations in which certain critical spots would become inaccessible if players were not coordinated. • corner is a rectangle (same shape as center) in the corner of the map. Its location tests h ...
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... E-mail: [email protected], [email protected] Abstract Artificial Life (ALife) uses biological knowledge and techniques to help solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems capable of performing ...
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Contextual Reasoning - Homepages of UvA/FNWI staff

... The field called Artificial Intelligence (AI) incorporates various aspirations. Some researchers want machines to do things that people call intelligent (making plans, communicating and cooperating with other computers and people, making and understanding jokes, directing movies, playing drums). Oth ...
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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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