Elad Liebman Sequential Decision-Making in Musical Intelligence 9783030305185

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Автор: Elad Liebman
Название: Sequential Decision-Making in Musical Intelligence
ISBN: 9783030305185
Издательство: Springer
Классификация:

Музыка

Техническая акустика и звукотехника

Автоматизированное проектирование (САПР)

Искусственный интеллект

ISBN-10: 303030518X
Обложка/Формат: Hardcover
Страницы: 206
Вес: 0.52 кг.
Дата издания: 2020
Серия: Studies in Computational Intelligence
Язык: English
Издание: 1st ed. 2020
Иллюстрации: 57 illustrations, color; 11 illustrations, black and white; xxv, 206 p. 68 illus., 57 illus. in color.
Размер: 234 x 156 x 14
Читательская аудитория: Professional & vocational
Основная тема: Engineering
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание:

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 hasnt 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 peoples 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.

Дополнительное описание: Introduction.- 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

Sequential Decision-Making in Musical Intelligence, Elad Liebman