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... by means of monitoring, planning, and strategic reasoning. In this domain, strategic reasoning more in particular helps in determining in resource-bounded situations if a go or no go should be given to, or to shift attention to, a certain evaluation of possible plans after an incident. An incident i ...
The Effect of Applying Artificial Intelligence in Shaping Marketing
The Effect of Applying Artificial Intelligence in Shaping Marketing

... need detecting a relationship between the information and the required results. The study also indicated that genetic algorithm s technique is used to identify the best values the system gets, therefore, it is recommended to be used with the projects that an idealistic outcome between the outputs. I ...
A Machine Learning Approach for Abstraction based on the Idea of
A Machine Learning Approach for Abstraction based on the Idea of

... bears in the field of DM at first. The term itself is highly controversial in research and used in different manners. Generally, Fulcher et al. and Karplus provide widely accepted definitions, which are also suitable within the context of this elaboration: x Nature-inspired method(s) + real-world (t ...
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Unsupervised Learning

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"Building Knowledge Automation Systems with Exsys CORVID" by

... decision-making knowledge and customized recommendations through an interactive interface, rather than just providing facts and data. These types of Web-enabled systems can be integrated into Web sites by a variety of techniques discussed in the book. The systems interact with users in a conversatio ...
IOSR Journal of Computer Engineering (IOSRJCE)
IOSR Journal of Computer Engineering (IOSRJCE)

... knowledge and interfaces. Expert systems also use human knowledge to solve problems that normally would require human intelligence. These expert systems represent the expertise knowledge as data or rules within the computer. These rules and data can be called upon when needed to solve problems. Book ...
CSCE 330 Programming Language Structures
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... “The exciting new effort to make computers think… machines with minds, in the full and literal sense.” (Haugeland, 1985) “[The automation of] activities that we associate with human thinking, activities such as decisionmaking, problem solving, learning…” (Bellman, ...
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Speech and Language Processing - Computer Science | CU
Speech and Language Processing - Computer Science | CU

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Artificial life: organization, adaptation and complexity

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finalReport - Suraj @ LUMS
finalReport - Suraj @ LUMS

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MICHAEL E. FARMER, PhD Associate Professor Computer Science

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toward memory-based reasoning - Computer Science, Columbia

... make a diagnosis; (2) a very small number of pa­ tients (one or two) are retrieved; (3) a significant number of patients are retrieved, and all have simi­ lar diagnoses; and (4) there are several diagnoses among the nearest n patients. At this point, we can make some observations: If the first case ...
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Formalisms for Multi-Agent Systems

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Logical and Probabilistic Knowledge Representation and Reasoning

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Economics/Humanitarian Issues - The IEEE Standards Association
Economics/Humanitarian Issues - The IEEE Standards Association

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APLICACIóN DE REDES NEuRONALES ARTIFICIALES A

... J. Jerez, L. Franco, E. Alba, A. Llombart-Cussac, A. Lluch, N. Ribelles, B. Munárriz and M. Martín. Improvement of Breast Cancer Relapse Prediction in High Risk Intervals Using Artificial Neural Networks. Breast Cancer Research and Treatment, 94, pp. 265--272 ...
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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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