Applied Intelligent Systems: New DirectionsJohn Fulcher, L. C. Jain Springer Science & Business Media, 5 mai 2004 - 325 pages Humans have always been hopeless at predicting the future...most people now generally agree that the margin of viability in prophecy appears to be 1 ten years. Even sophisticated research endeavours in this arena tend to go 2 off the rails after a decade or so. The computer industry has been particularly prone to bold (and often way off the mark) predictions, for example: ‘I think there is a world market for maybe five computers’ Thomas J. Watson, IBM Chairman (1943), ‘I have traveled the length and breadth of this country and talked with the best people, and I can assure you that data processing is a fad that won’t last out the year’ Prentice Hall Editor (1957), ‘There is no reason why anyone would want a computer in their home’ Ken Olsen, founder of DEC (1977) and ‘640K ought to be enough for anybody’ Bill Gates, CEO Microsoft (1981). 3 The field of Artificial Intelligence – right from its inception – has been particularly plagued by ‘bold prediction syndrome’, and often by leading practitioners who should know better. AI has received a lot of bad press 4 over the decades, and a lot of it deservedly so. How often have we groaned in despair at the latest ‘by the year-20xx, we will all have...(insert your own particular ‘hobby horse’ here – e. g. |
Table des matières
I | 1 |
II | 2 |
III | 3 |
IV | 5 |
V | 6 |
VI | 7 |
VII | 10 |
VIII | 12 |
XCI | 166 |
XCII | 167 |
XCIII | 170 |
XCIV | 171 |
XCV | 175 |
XCVI | 176 |
XCVII | 177 |
XCVIII | 179 |
IX | 14 |
X | 15 |
XI | 17 |
XII | 18 |
XIII | 20 |
XIV | 23 |
XV | 30 |
XVI | 33 |
XVIII | 34 |
XIX | 35 |
XX | 36 |
XXII | 37 |
XXIII | 38 |
XXV | 39 |
XXVI | 40 |
XXVII | 41 |
XXVIII | 43 |
XXIX | 45 |
XXX | 47 |
XXXIII | 51 |
XXXIV | 54 |
XXXV | 59 |
XXXVI | 60 |
XXXVII | 62 |
XXXVIII | 64 |
XXXIX | 65 |
XL | 66 |
XLII | 68 |
XLIII | 69 |
XLIV | 71 |
XLVI | 72 |
XLVII | 73 |
XLVIII | 74 |
XLIX | 75 |
LI | 76 |
LII | 77 |
LIII | 78 |
LIV | 81 |
LV | 83 |
LVI | 85 |
LVII | 86 |
LVIII | 95 |
LIX | 97 |
LX | 98 |
LXI | 102 |
LXII | 104 |
LXIII | 105 |
LXIV | 107 |
LXV | 109 |
LXVI | 113 |
LXVII | 115 |
LXVIII | 120 |
LXIX | 121 |
LXX | 130 |
LXXII | 131 |
LXXIII | 133 |
LXXIV | 134 |
LXXV | 136 |
LXXVI | 138 |
LXXVII | 139 |
LXXIX | 140 |
LXXX | 141 |
LXXXI | 143 |
LXXXII | 144 |
LXXXIII | 145 |
LXXXV | 146 |
LXXXVI | 154 |
LXXXVII | 155 |
LXXXVIII | 157 |
LXXXIX | 158 |
XC | 165 |
XCIX | 181 |
C | 182 |
CI | 183 |
CII | 187 |
CIII | 189 |
CIV | 191 |
CV | 193 |
CVI | 195 |
CVIII | 196 |
CIX | 197 |
CX | 198 |
CXI | 199 |
CXIII | 201 |
CXIV | 202 |
CXV | 204 |
CXVI | 208 |
CXVII | 209 |
CXVIII | 210 |
CXIX | 211 |
CXX | 214 |
CXXI | 219 |
CXXII | 222 |
CXXIII | 228 |
CXXIV | 231 |
CXXV | 233 |
CXXVI | 234 |
CXXVII | 240 |
CXXVIII | 246 |
CXXIX | 247 |
CXXXI | 255 |
CXXXII | 256 |
CXXXIII | 257 |
CXXXIV | 258 |
CXXXVI | 260 |
CXXXVII | 262 |
CXXXVIII | 263 |
CXL | 264 |
CXLI | 265 |
CXLII | 267 |
CXLIV | 269 |
CXLV | 270 |
CXLVI | 271 |
CXLVII | 272 |
CXLVIII | 273 |
CXLIX | 275 |
CL | 276 |
CLII | 281 |
CLIII | 282 |
CLV | 284 |
CLVI | 285 |
CLVII | 286 |
CLVIII | 288 |
CLIX | 289 |
CLX | 290 |
CLXI | 291 |
CLXII | 292 |
CLXIII | 293 |
CLXIV | 295 |
CLXVII | 297 |
299 | |
CLXX | 301 |
CLXXI | 302 |
CLXXII | 303 |
CLXXIII | 305 |
CLXXIV | 306 |
CLXXV | 312 |
CLXXVI | 317 |
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Expressions et termes fréquents
2D decision surface ants applications approach argument Artificial Intelligence Artificial Neural Networks Australia Blind Signal Separation blind source separation case-based reasoning clustering coefficients cognitive combination complex Computer Conf Artificial Intelligence Data Mining data set decision support systems defined described developed domain environment equation error evaluation Evolutionary Algorithm example Expert Systems forecasting function Gamebots Gaussian human ICA algorithms IEEE Independent Component Analysis individuals Intelligence and Law intelligent agents Intl Conf Artificial knowledge based learning legal decision support legal knowledge memory method mobile robot multivariate NAHONN negotiation neural network group neuron ontology open textured Optimisation output neuron parameter particle path segments pattern performance pheromone PHONN polynomial prediction problem Proc rainfall estimation result Roomba rules sensors sequence series analysis Signal Processing simulation solution source separation statistical swarm techniques test input transaction update values variables vector Zeleznikow
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