000			03209nam a22004335i 4500
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003			ES-MaUEC
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007			cr nn 008mamaa
008			220601s2018 sz | s |||| 0|eng d
020			_a9783031015786
024	7		_a10.1007/978-3-031-01578-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aHD30.23 _b2018 EB
100	1		_aRosenfeld, Ariel _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686633
245	1	0	_aPredicting Human Decision-Making _bFrom Prediction to Action _cby Ariel Rosenfeld, Sarit Kraus.
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XVI, 134 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aPreface -- Acknowledgments -- Introduction -- Utility Maximization Paradigm -- Predicting Human Decision-Making -- From Human Prediction to Intelligent Agents -- Which Model Should I Use? -- Concluding Remarks -- Bibliography -- Authors' Biographies -- Index .
520			_aHuman decision-making often transcends our formal models of "rationality." Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures-from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9670862 _aPredicción, Teoría de la
650		7	_2embne _9141176 _aToma de decisiones _xModelos matemáticos
700	1		_aKraus, Sarit _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686634
776	0	8	_iPrinted edition: _z9783031000232
776	0	8	_iPrinted edition: _z9783031004506
776	0	8	_iPrinted edition: _z9783031027062
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01578-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI