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Artificial Intelligence I: introduction
Artificial Intelligence I: introduction

... In neural networks In uncertain reasoning and expert systems: Bayesian network ...
Nicolas Boulanger-Lewandowski
Nicolas Boulanger-Lewandowski

A Believable Agent for First-Person Shooter Games
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A Believable Agent for First-Person Shooter Games
A Believable Agent for First-Person Shooter Games

... Abstract In this paper, we present a principled approach to constructing believable game players that relies on a cognitive architecture. The resulting agent is capable of playing the game Urban Combat in a plausible manner when faced with similar situations as its human counterparts. We discuss how ...
Alphabet Pattern Recognition using Spiking Neural
Alphabet Pattern Recognition using Spiking Neural

... Abstract— Pattern Recognition is one of the very important and active traits or it is a branch of artificial intelligence. It offers advantages such as fraud reduction, it increases reliability and also its a cheap technology. The various applications of pattern recognition is used in the security p ...
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Power Point Slides of Chapter 8

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Computer Security - University of Waterloo
Computer Security - University of Waterloo

... Introduces novel approaches for computational intelligence based techniques including: knowledge based reasoning, expert systems, fuzzy inferencing and connectionist modeling based on artificial neural networks. The focus is on the use of soft computing approaches to deal effectively with real world ...
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Biological Inspiration for Artificial Neural Networks

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Theme: Artificial Intelligence and Its Benefits to Society…

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Kevin_Noonan_AIProject

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Outstanding Achievement @ IEEE Computational Intelligence
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... programs MoGo and Many Faces of Go even won against human (6D) in 13  13 Go with handicap 2 [6, 8]. From the games results at the competition, we know that the computer Go programs won 9 out of the total 22 games. The average performance of the computer Go programs is fast approaching to the profe ...
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... 1.1 Simulations and analysis of neural network models with emphasis on attractor memory networks General theme: There have been a range of theoretical concepts of brain computations proposed in computational neuroscience. Among the connectionist (network-based) approaches to modelling brain function ...
Artificial Intelligence Informed or Heuristic Search Heuristic
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1950 – birth of AI, Turing test - Department of Intelligent Systems
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... Multiple worlds Schrëding equation, Heisenberg’s principle, collapse On the other hand, the collapse is considered as redundant in •the Bohm interpretation •the Many-Worlds Interpretation Multiverse Occam’s razor? 100x black matter needed String theory ...
Outline of the support document
Outline of the support document

A Tutorial on Cognitive Network Process for Business Applications
A Tutorial on Cognitive Network Process for Business Applications

... Email: [email protected] Analytic Hierarchy Process (AHP) is increasingly applied to many applications. Knowledge representation of pairwise reciprocal matrix used in AHP, however, is still open to discuss. This talk discusses the basic concepts and usages of AHP with its limitations. This tal ...
Common Sense - Myreaders.info
Common Sense - Myreaders.info

... attempt to implement McCarthy's idea, but faced difficulties because : ...
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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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