000 | 03927nam a22004935i 4500 | ||
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001 | 978-3-642-36406-8 | ||
003 | DE-He213 | ||
005 | 20200421111839.0 | ||
007 | cr nn 008mamaa | ||
008 | 130202s2013 gw | s |||| 0|eng d | ||
020 |
_a9783642364068 _9978-3-642-36406-8 |
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024 | 7 |
_a10.1007/978-3-642-36406-8 _2doi |
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050 | 4 | _aQ342 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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082 | 0 | 4 |
_a006.3 _223 |
245 | 1 | 0 |
_aDecision Making and Imperfection _h[electronic resource] / _cedited by Tatiana V. Guy, Miroslav Karny, David Wolpert. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013. |
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300 |
_aXV, 187 p. 72 illus., 24 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Computational Intelligence, _x1860-949X ; _v474 |
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505 | 0 | _aDynamic Bayesian Combination of Multiple Imperfect Classifiers -- Distributed Decision Making by Categorically-Thinking Agents -- Automated Preference Elicitation for Decision Making -- Counter-Factual Reinforcement Learning: How To Model Decision-Makers that Anticipate the Future -- Effect of Emotion and Personality on Deviation from Purely Rational Decision-Making -- An Adversarial Risk Analysis Model for an Autonomous Imperfect Decision Agent. | |
520 | _aDecision making (DM) is ubiquitous in both natural and artificial systems. The decisions made often differ from those recommended by the axiomatically well-grounded normative Bayesian decision theory, in a large part due to limited cognitive and computational resources of decision makers (either artificial units or humans). This state of a airs is often described by saying that decision makers are imperfect and exhibit bounded rationality. The neglected influence of emotional state and personality traits is an additional reason why normative theory fails to model human DM process.   The book is a joint effort of the top researchers from different disciplines to identify sources of imperfection and ways how to decrease discrepancies between the prescriptive theory and real-life DM. The contributions consider:   �          how a crowd of imperfect decision makers outperforms experts' decisions;   �          how to decrease decision makers' imperfection by reducing knowledge available;   �          how to decrease imperfection via automated elicitation of DM preferences;   �          a human's limited willingness to master the available decision-support tools as an additional source of imperfection;   �          how the decision maker's emotional state influences the rationality;  a DM support of edutainment robot based on its system of values and respecting emotions.   The book will appeal to anyone interested in the challenging topic of DM theory and its applications. . | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aComputational intelligence. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
700 | 1 |
_aGuy, Tatiana V. _eeditor. |
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700 | 1 |
_aKarny, Miroslav. _eeditor. |
|
700 | 1 |
_aWolpert, David. _eeditor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783642364051 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-949X ; _v474 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-642-36406-8 |
912 | _aZDB-2-ENG | ||
942 | _cEBK | ||
999 |
_c55479 _d55479 |