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Supporting Problem Solving in PBL - Purdue e-Pubs
Supporting Problem Solving in PBL - Purdue e-Pubs

... necessary for success in a PBL environment. Because PBL represents a significant shift in learning for most students, they require support in adapting their learning methods. We cannot assume that learners are naturally skilled in problem solving, especially complex and ill-structured problems such ...
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Goal-Driven Autonomy in a Navy Strategy Simulation
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... duration (e.g., follow another vessel). Therefore, agents interacting with the simulation must reason about instantaneous occurrences and continuous effects. Attempting missions in the TAO Sandbox autonomously is a continuous planning problem (desJardins et al. 1999). Opportunities and failures ari ...
Paul R. Watkins, PhD - CV - College of Education | Idaho State
Paul R. Watkins, PhD - CV - College of Education | Idaho State

... "Auditor's Use of Analytical Review in Audit Program Design", The Accounting Review, January 1988 (w. T. Mock and S. Biggs) "Perspective on Expert Systems", Expert Systems Review, July 1988 "Expert Systems Applications and Opportunities in Finance", Applied Expert Systems, August 1988 "Expert System ...
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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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