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Sinopsis
Over 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.
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De la contraportada
Over 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.
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Sequential Decision-Making in Musical Intelligence
Publicado por
Springer International Publishing, 2020
ISBN 10: 303030521X
ISBN 13: 9783030305215
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Kartoniert / Broschiert
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Focuses on two aspects of musical intelligence: music recommendation and human-agent interaction in the context of music Covers topics such as the design of better music playlist recommendation algorithms and algorithms . Nº de ref. del artículo: 448678430
Cantidad disponible: Más de 20 disponibles
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Sequential Decision-Making in Musical Intelligence
Publicado por
Springer International Publishing Okt 2020, 2020
ISBN 10: 303030521X
ISBN 13: 9783030305215
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Over 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. 232 pp. Englisch. Nº de ref. del artículo: 9783030305215
Cantidad disponible: 2 disponibles
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Sequential Decision-Making in Musical Intelligence
Publicado por
Springer International Publishing, 2020
ISBN 10: 303030521X
ISBN 13: 9783030305215
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Over 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'tbeen 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. Nº de ref. del artículo: 9783030305215
Cantidad disponible: 1 disponibles
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Sequential Decision-Making in Musical Intelligence (Studies in Computational Intelligence)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Condición: New. In. Nº de ref. del artículo: ria9783030305215_new
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Sequential Decision-Making in Musical Intelligence
Publicado por
Springer International Publishing, Springer International Publishing Okt 2020, 2020
ISBN 10: 303030521X
ISBN 13: 9783030305215
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -Over 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 232 pp. Englisch. Nº de ref. del artículo: 9783030305215
Cantidad disponible: 2 disponibles
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Sequential Decision-Making in Musical Intelligence
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Condición: New. pp. XXV, 206 68 illus., 57 illus. in color. 1 Edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26384559177
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Sequential Decision-making in Musical Intelligence
Librería: Revaluation Books, Exeter, Reino Unido
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Paperback. Condición: Brand New. 232 pages. 9.25x6.10x0.55 inches. In Stock. Nº de ref. del artículo: x-303030521X
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Sequential Decision-Making in Musical Intelligence
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
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Condición: New. Print on Demand pp. XXV, 206 68 illus., 57 illus. in color. Nº de ref. del artículo: 379344790
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Sequential Decision-Making in Musical Intelligence (Studies in Computational Intelligence)
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Condición: New. Nº de ref. del artículo: ABLIING23Mar3113020012977
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Sequential Decision-Making in Musical Intelligence
Impresión bajo demandaLibrería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 4 de 5 estrellas
Condición: New. PRINT ON DEMAND pp. XXV, 206 68 illus., 57 illus. in color. Nº de ref. del artículo: 18384559171
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