Abstract
In Chap. 5 I discussed how the impact of music on human decision-making can be modeled. Subsequently in Chap. 6 I discussed how this impact can be leveraged by an agent to engender better interaction with a person. However, that is only one facet of person-agent interaction in musical context. The other includes a scenario in which people and machines actively collaborate in music generation. What would such an interaction be like? An important aspect of person-agent interaction, or of agents interacting with multiple people and/or other agents, is that of reasoning about preferences. Particularly in a domain such as music generation, people’s subjective tastes play a pivotal role, and reasoning about them when trying to collaborate is critical. This train of thought leads to a deeper question: how can multiple agents reason with each other in a shared task while also maintaining individual preferences that may be at odds with the shared task and with the preferences of others? Studying the balance between shared tasks and individual preferences in multiagent interaction is a significant step in fulfillment of Contribution 4 of this book, building towards multiagent music interaction as a meaningful step towards person-agent music generation.
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Notes
- 1.
Relating the scale to actual notes, 0 denotes C, 1 denotes \(C\#\), 2 denotes D and so forth up to 11 = B.
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Liebman, E. (2020). Multiagent Collaboration Learning: A Music Generation Test Case. 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_7
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