Abstract
In the previous chapter I established, in fulfillment of Contribution 3 of this work, that music indeed alters people’s decision-making process in a nontrivial way, and that this effect can be modeled computationally. In this chapter I take this research thread two steps further: first to provide evidence that the impact of music on human decision-making goes beyond binary decisions; and second to show results suggesting that this effect can be modeled in real-time by a learning agent in order to induce better human-agent interaction. One of the core contributions of this book, described in Sect. 1.1, is the development of approaches and techniques for multiagent collaboration in musical environments, and specifically human-agent collaboration, constituting Contribution 4. Providing evidence that an agent can represent music as part of its world state and utilize this knowledge for better interaction is a substantial component of fulfilling Contribution 4. The other aspect of this contribution, which is the study of how multiple agents with varying tastes (which are not directly known to the other agents) can collaborate jointly, is studied in Chap. 7.
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A 0.1 threshold for testing the significance of p-values is accepted in the context of relatively small samples sizes. Nonetheless, we strive to use these measures responsibly in our choice of language, thus using the equally common term “borderline significance” to describe results with p-value < 0.1 but > 0.05.
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Liebman, E. (2020). Impact of Music on Person-Agent Interaction. 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_6
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DOI: https://doi.org/10.1007/978-3-030-30519-2_6
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