000			03865nam a22004335i 4500
988			_aSpringer_Robotics_2020
999			_c116979 _d116979 _x1
001			116979
003			ES-MaUEC
005			20240111050159.0
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
008			191001s2020 gw | s |||| 0|eng d
020			_a9783030305192
024	7		_a10.1007/978-3-030-30519-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aML74 _b2020 EB
100	1		_aLiebman, Elad _eautor _9672088
245	1	0	_aSequential decision-making in musical intelligence _cby Elad Liebman
250			_aFirst edition
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XXV, 206 páginas) _b68 ilustraciones, 57 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	1		_aStudies in Computational Intelligence _x1860-949X _v857
490	0		_aTechnologies and Robotics (Springer-42732)
505	0		_aIntroduction -- Background -- Playlist Recommendation -- Algorithms for Tracking Changes In Preference Distributions -- Modeling the Impact of Music on Human Decision-Making -- Impact of Music on Person-Agent Interaction -- Multiagent Collaboration Learning: A Music Generation Test Case -- Related Work and a Taxonomy of Musical Intelligence Tasks -- Conclusion and Future Work.
520	3		_aOver the past 60 years, artificial intelligence has grown from an academic field of research to a ubiquitous array of tools used in everyday technology. Despite its many recent successes, certain meaningful facets of computational intelligence have yet to be thoroughly explored, such as a wide array of complex mental tasks that humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over recent decades, many researchers have used computational tools to perform tasks like genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents able to mimic (at least partially) the complexity with which humans approach music. One key aspect that hasn't been sufficiently studied is that of sequential decision-making in musical intelligence. Addressing this gap, the book focuses on two aspects of musical intelligence: music recommendation and multi-agent interaction in the context of music. Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, the work presented in this book also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as content recommendation. Showing the generality of insights from musical data in other contexts provides evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall value of taking a sequential decision-making approach in settings previously unexplored from this perspective.
650		7	_2embne _aMúsica _xProceso de datos _9138603
650		7	_2embne _aArte y tecnología _9144821
650		7	_2embne _aInteligencia artificial _9413115
776	0	8	_iPrinted edition: _z9783030305185
776	0	8	_iPrinted edition: _z9783030305208
776	0	8	_iPrinted edition: _z9783030305215
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-30519-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_dz _feng _ggw _h0 _b01/2020 _eel _zSI