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Details - John Franco
Details - John Franco

... Abstract— For solving problems of robot navigation over unknown and changing terrain, many algorithms have been invented. For example, D* Lite, which is a dynamic, incremental search algorithm, is the most successful one. The improved performance of the D* Lite algorithm over other replanning algori ...
Experiment
Experiment

... Where pi(e|f) is the phrase translation model corresponding to the i-th CWSs. αi is the weight and S is the total number of models. ...
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... – Consists of related technologies that try to simulate and reproduce human thought and behavior – Includes thinking, speaking, feeling, and reasoning ...
this PDF file - BRAIN. Broad Research in Artificial
this PDF file - BRAIN. Broad Research in Artificial

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artificial intelligence research in particle accelerator control

... simulator, to generate large amounts of data. They eventually compared the results among several fundamentally different types of algorithms, including least squares and hybrid neural networks with real data that were obtained from Brookhaven National Laboratory. ...
Yale University “Imagine that you work for a computer company. You
Yale University “Imagine that you work for a computer company. You

... systems could be full artificial moral agents. A session was dedicated to criteria for attributing moral agency, and whether a mindless system can be a moral agent. This opened up into a broader discussion of what machines could understand, emotional intelligence, embodied cognition, and social skil ...
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Presentación de PowerPoint

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Chapter 1

... – What factors create these new pressures? (From our environ, business partners, customers) ...
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Chapter 02 for Neuro-Fuzzy and Soft Computing

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... human and a machine. The interrogator is physically removed from the other two participants. He can communicate with each of them by way of a teletype, he does not however, know which participant is machine and which is human. His task is to establish which one is the machine and which is the human. ...
Global Optimization for Multiple Agents - Infoscience
Global Optimization for Multiple Agents - Infoscience

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the application of artificial intelligence methods in heat - QRC

... elements from the examined problem area. Deduction component task is to choose knowledge elements from knowledge base and, at the end, connect them into meaningful deduction. Deduction quality which knowledge mechanism is bringing depends on, so called, expert system communication elements. Their ma ...
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... a global vision for the future. The advancement of current technologies in the fields such as data and telecommunications, ubiquitous Internet access and sensor technologies combined with the new revolutionary explorations and concepts in biotechnology and nanotechnology, computer human interface-in ...
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Design And Implementation Of Fuzzy Rule

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Artificial Intelligence Winter 2004

...  In a subjective situation there is no exact original (like “this is stupid”), only a subjective impression is reflected.  In order put subjective vagueness on solid foundations the reasoning in the model is replaced by experiments with the individuals who express their subjective opinions.  From ...
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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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