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Introduction to Hybrid Systems – Part 1
Introduction to Hybrid Systems – Part 1

... environment. The performance element (PE) is the actual controller. The learning element.(LE) updates the knowledge in the PE . The LE has access to the environment, the past states and the performance measure. It updates the PE. The examines the external performance and provides feedback to the LE. ...
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... Known as V.R., Artificial Reality, and Virtual Environments VRML Virtual Reality Programming Language used to create real-time animated scenes Authoring Programs VRML programs widely used to create Web-based VR applications Applications Simulate experiences or training environments ...
AAAI-13 Exhibitor Information
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... AAAI-13 Exhibitor Information On behalf of AAAI, we invite you to exhibit at the Twenty-Seventh AAAI Conference on Artificial Intelligence to be held July 14 – 18, 2013 in Bellevue, Washington, USA. Each year the AAAI conference brings together about 1,000 AI researchers and practitioners from aroun ...
AAAI-14 Exhibitor Information
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... AAAI-14 Exhibitor Information On behalf of AAAI, we invite you to exhibit at the Twenty-Eighth AAAI Conference on Artificial Intelligence to be held July 27 – 31, 2014 in Quebec City, Quebec, Canada. Each year the AAAI conference brings together about 1,000 AI researchers and practitioners from arou ...
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... • Understand the theory behind the techniques, knowing which techniques to apply when (and why) • Become familiar with a range of applications of AI, including “classic” and current systems. January 11, 2006 ...
Am I Human? - cs.Virginia
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A Methodology for Modeling and Representing Expert Knowledge

... learned from a domain expert) to being directly taught by a domain expert that receives limited or no support from a knowledge engineer. The investigated approach, called Disciple (Tecuci, 1998; Tecuci et al., 1999), relies on developing a very capable learning and reasoning agent that can collabora ...
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A Survey on Artificial Intelligence and Robotics

... the work in AI and robotics is certainly very difficult to establish; however, the problems to be addressed in order to build intelligent robots are clearly identified by the research community, and the development of robots is again viewed as a prototypical case of AI system[5]. II. LITERATURE SURV ...
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AI winter

In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research. The term was coined by analogy to the idea of a nuclear winter. The field has experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or decades later. There were two major winters in 1974–80 and 1987–93 and several smaller episodes, including: 1966: the failure of machine translation, 1970: the abandonment of connectionism, 1971–75: DARPA's frustration with the Speech Understanding Research program at Carnegie Mellon University, 1973: the large decrease in AI research in the United Kingdom in response to the Lighthill report, 1973–74: DARPA's cutbacks to academic AI research in general, 1987: the collapse of the Lisp machine market, 1988: the cancellation of new spending on AI by the Strategic Computing Initiative, 1993: expert systems slowly reaching the bottom, and 1990s: the quiet disappearance of the fifth-generation computer project's original goals.The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the ""American Association of Artificial Intelligence""). It is a chain reaction that begins with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research. At the meeting, Roger Schank and Marvin Minsky—two leading AI researchers who had survived the ""winter"" of the 1970s—warned the business community that enthusiasm for AI had spiraled out of control in the '80s and that disappointment would certainly follow. Three years later, the billion-dollar AI industry began to collapse.Hypes are common in many emerging technologies, such as the railway mania or the dot-com bubble. An AI winter is primarily a collapse in the perception of AI by government bureaucrats and venture capitalists. Despite the rise and fall of AI's reputation, it has continued to develop new and successful technologies. AI researcher Rodney Brooks would complain in 2002 that ""there's this stupid myth out there that AI has failed, but AI is around you every second of the day."" In 2005, Ray Kurzweil agreed: ""Many observers still think that the AI winter was the end of the story and that nothing since has come of the AI field. Yet today many thousands of AI applications are deeply embedded in the infrastructure of every industry."" He added: ""the AI winter is long since over.""
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