Abstract
The purpose of this section is to provide the reader with context helpful for understanding the contributions discussed in this manuscript. Whereas a fuller review of the (extensive) literature lying at the intersection of AI and music is provided in Chap. 8, in this chapter I focus on the key frameworks utilized in this book. In Sect. 2.1 I present the key learning framework used in this work, which is reinforcement learning. In Sect. 2.2 I provide a broad overview of music AI research, with a particular focus music recommendation, which is a recurring theme in this book (particularly in Chaps. 3 and 4, but also relevant to Chap. 7), and on the gaps this book aims to address. Because human behavior with respect to music is another key aspect of this book (particularly in Chaps. 3, 5, and 6), in Sect. 2.3 I provide context relating to the impact of music on human behavior, and discuss how this book addresses gaps in this respect as well.
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Notes
- 1.
If the states are not fully revealed to the agent, then the problem becomes a Partially Observable MDP (or POMDP). Though many real world problems can be modeled more accurately as POMDPs rather than regular MDPs, this distinction is beyond the scope of this book.
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Liebman, E. (2020). Background. In: Sequential Decision-Making in Musical Intelligence. Studies in Computational Intelligence, vol 857. Springer, Cham. https://doi.org/10.1007/978-3-030-30519-2_2
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