LEADER 04532nam 22006375 450 001 9910483903703321 005 20200704062331.0 010 $a3-030-30519-8 024 7 $a10.1007/978-3-030-30519-2 035 $a(CKB)4100000009382630 035 $a(DE-He213)978-3-030-30519-2 035 $a(MiAaPQ)EBC5941368 035 $a(PPN)243769490 035 $a(EXLCZ)994100000009382630 100 $a20191001d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSequential Decision-Making in Musical Intelligence /$fby Elad Liebman 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XXV, 206 p. 68 illus., 57 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v857 311 $a3-030-30518-X 320 $aIncludes bibliographical references. 327 $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. 330 $aOver the past 60 years, artificial intelligence has grown from an academic field of research to a ubiquitous array of tools used in everyday technology. Despite its many recent successes, certain meaningful facets of computational intelligence have yet to be thoroughly explored, such as a wide array of complex mental tasks that humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over recent decades, many researchers have used computational tools to perform tasks like genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents able to mimic (at least partially) the complexity with which humans approach music. One key aspect that hasn't been sufficiently studied is that of sequential decision-making in musical intelligence. Addressing this gap, the book focuses on two aspects of musical intelligence: music recommendation and multi-agent interaction in the context of music. Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, the work presented in this book also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as content recommendation. Showing the generality of insights from musical data in other contexts provides evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall value of taking a sequential decision-making approach in settings previously unexplored from this perspective. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v857 606 $aComputational intelligence 606 $aAcoustical engineering 606 $aMusic 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aEngineering Acoustics$3https://scigraph.springernature.com/ontologies/product-market-codes/T16000 606 $aMusic$3https://scigraph.springernature.com/ontologies/product-market-codes/417000 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aAcoustical engineering. 615 0$aMusic. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aEngineering Acoustics. 615 24$aMusic. 615 24$aArtificial Intelligence. 676 $a781.028563 676 $a780.28563 700 $aLiebman$b Elad$4aut$4http://id.loc.gov/vocabulary/relators/aut$01224927 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483903703321 996 $aSequential Decision-Making in Musical Intelligence$92844159 997 $aUNINA