| 000 | 03336nam a2200517 i 4500 | ||
|---|---|---|---|
| 001 | 6267472 | ||
| 003 | IEEE | ||
| 005 | 20220712204715.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151224s2008 maua ob 001 eng d | ||
| 010 | _z 91183100 (print) | ||
| 020 |
_a9780262288507 _qelectronic |
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| 020 |
_z9780262512787 _qprint |
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| 035 | _a(CaBNVSL)mat06267472 | ||
| 035 | _a(IDAMS)0b000064818b44ac | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aQA76.7 _b.K33 1990eb |
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| 082 | 0 | 0 |
_a005.1 _220 |
| 100 | 1 |
_aKaelbling, Leslie Pack, _eauthor. _922979 |
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| 245 | 1 | 0 |
_aLearning in embedded systems / _cby Leslie Pack Kaelbling. |
| 264 | 1 |
_aStanford, Calif. : _bDept. of Computer Science, Stanford University, _c[c1990] |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2008] |
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| 300 |
_a1 PDF (xx, 200 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 |
_aReport ; _vno. STAN-CS-90-1326 |
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| 500 | _aCover title. | ||
| 500 | _a"June 1990." | ||
| 504 | _aIncludes bibliographical references (p. 191-200). | ||
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aLearning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases.Kaelbling investigates a rapidly expanding branch of machine learning known as reinforcement learning, including the important problems of controlled exploration of the environment, learning in highly complex environments, and learning from delayed reward. She reviews past work in this area and presents a number of significant new results. These include the intervalestimation algorithm for exploration, the use of biases to make learning more efficient in complex environments, a generate-and-test algorithm that combines symbolic and statistical processing into a flexible learning method, and some of the first reinforcement-learning experiments with a real robot.Leslie Pack Kaelbling is Assistant Professor in the Computer Science Department at Brown University. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/24/2015. | ||
| 650 | 0 |
_aEmbedded computer systems _xProgramming. _914089 |
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| 650 | 0 |
_aComputer algorithms. _94534 |
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| 655 | 0 |
_aElectronic books. _93294 |
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| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _922980 |
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| 710 | 2 |
_aMIT Press, _epublisher. _922981 |
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| 776 | 0 | 8 |
_iPrint version _z9780262512787 |
| 830 | 0 |
_aReport (Stanford University. Computer Science Department) ; _vno. STAN-CS-90-1326. _922982 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267472 |
| 942 | _cEBK | ||
| 999 |
_c73126 _d73126 |
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