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Problem Difficulty and the Phase Transition in Heuristic Search
Problem Difficulty and the Phase Transition in Heuristic Search

... prove that no solution exists. We present results only for n = 100000 as the other plots show the same behavior. The Phase Transition. Figure 1a shows the probability that a solution is found plotted against γ for 100K-state random problems. As we increase γ, there is a clear phase transition in sol ...
Research and Development of Granular Neural Networks
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... of the recent research focuses of the field of GrC. The neural network is one kind of network system by simulating human brain information processing mechanism based on the development of modern biology research, which is also one of the soft computing techniques. As artificial neural network has di ...
The Singularity: A Philosophical Analysis
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... This intelligence explosion is sometimes combined with another idea, which we might call the “speed explosion”. The argument for a speed explosion starts from the familiar observation that computer processing speed doubles at regular intervals. Suppose that speed doubles every two years and will do ...
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... be used in problem solving when either its PLB condition or its PUB condition is satisfied, as summarized in Figure 4. Indeed, let us assume that, after having learned the rule from Figure 3, Disciple attempts to “Determine whether Mark White can be a PhD advisor for Tom Evan in Information Security ...
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Combinations of Case-Based Reasoning with Other Intelligent Methods (short paper)

Paper Title (use style: paper title)
Paper Title (use style: paper title)

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... his team have devised several systems which are improving the diagnosis and management of rectal and prostate cancers and carotid artery disease. They have generated a mind-boggling 25 patents and two spin-off companies, as well as licensing agreements with nine different companies. “Our core techno ...
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Curriculum vitae - Department of Computer Science
Curriculum vitae - Department of Computer Science

... 4. Dagstuhl Seminar on Formal Models of Belief Change in Rational Agents, Schloss Dagstuhl, Germany, August, 2009. 5. Dagstuhl Perspectives Workshop on Theory and Practice of Argumentation Systems, Schloss Dagstuhl, Germany, 20-23 January 2008. 6. Invitation to attend a Dagstuhl Seminar on Formal Mo ...
What`s Hot in Heuristic Search? - Association for the Advancement
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... This extends traditional best-first search algorithms such as A* (Hart, Nilsson, and Raphael 1968), which operates a single open list sorted according to the information provided by a single heuristic. Current state-of-the-art planners use this multiple open list approach (Röger and Helmert 2010). R ...
Advanced Research into AI Ising Computer (PDF format, 212KB)
Advanced Research into AI Ising Computer (PDF format, 212KB)

... a performance index under given conditions. A characteristic of combinatorial optimization problems is that the number of candidate solutions increases explosively the greater the number of parameters that define the problem. As the number of parameters in AI computation is increasing, the number of ...
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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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