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In the previous chapter, I discussed the problem of music recommendation as a sequential decision-making problem, as people’s preferences and expectations are informed by what has been played up to any given point in time. However, there are other ways in which people’s preferences and expectations are influenced by music. A relevant question in the context of studying interactive processes between people and automated systems is how background information, and music in particular, impact the way people make decisions. This chapter focuses on this question in two distinct contexts which engage different decision-making processes.

In order to better understand the contributions of this book it is important to put them in context. Given the breadth and complexity of research work at the intersection of AI research and music informatics, a need arises to find useful ways of breaking down and organizing previous research. Indeed, in this section I will not only introduce a broad overview of the related literature, but also propose a unified framework for parsing this varied and complex body of work, delineating a taxonomy for music AI tasks, and mapping out the overall state of the art. In doing so, this chapter constitutes Contribution 1 of this book.

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.

As described in Sect. 1.1, one of the core problems in music recommendation over time, and in content recommendation in general, is that the distributional properties of music, and people’s musical tastes, change over time in ways that are nontrivial to track and predict. This informational challenge is a special case of concept drift—the change, either abrupt or gradual, in the underlying structure of data. Concept drift is a common and fundamental problem in machine learning in general, so any solutions designed to combat drift in content recommendation, even ones specific for that setting, are broadly applicable to ML research in general. In this chapter I focus on the application of reinforcement learning approaches to handle concept drift, specifically through model retraining, both in its general context and directly with respect to tracking people’s temporal listening habits as reflected by a real world dataset.

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.

UT Austin Villa is a robot soccer team that has competed in the annual RoboCup soccer competitions since 2003. The team has won several championships and has inspired research contributions spanning many topics in robotics and artificial intelligence. This article summarizes some of these research contributions and provides a snapshot into the current development status of the team. Educational uses of the team's code bases are also presented.

There is great interest in understanding whether and how mood influences affective processing. Results in the literature have been mixed: some studies show mood-congruent processing but others do not. One limitation of previous work is that decision components for affective processing and responses biases are not dissociated. The present study explored the roles of affective processing and response biases using a drift-diffusion model (DDM) of simple choice. In two experiments, participants decided if words were emotionally positive or negative while listening to music that induced positive or negative mood. The behavioural results showed weak, inconsistent mood-congruency effects. In contrast, the DDM showed consistent effects that were selectively driven by an a-priori bias in response expectation, suggesting that music-induced mood influences expectations about the emotionality of upcoming stimuli, but not the emotionality of the stimuli themselves. Implications for future studies of emotional classification and mood are subsequently discussed.

This paper presents a novel algorithmic approach to music performance analysis. Previous attempts to use algorithmic tools in this field focused typically on tempo and dynamics alone. We base our analysis on ten different performance categories (such as bowing, vibrato and durations). We adapt phylogenetic analysis tools to resolve the inherent inconsistencies between these categories, and describe the relationships between performances. Taking samples from 29 different performances of two pieces from Bach's sonatas for solo violin, we construct a ‘phylogenetic’ tree, representing the relationship between those performances. The tree supports several interesting relations previously conjectured by the musicology community, such as the importance of date of birth and recording period in determining interpretative style. Our work also highlights some unexpected inter-connections between performers, and challenges previous assumptions regarding the significance of educational background and affiliat...

In this paper we study a bin-based estimation method of the amount of effort associated with code development. We investigate the following 3 variants to define the bins: (1) the same amount of data in a bin (SVM same #), (2) the same range for each bin (SVM same range) and (3) the bins made by Ward’s method (SVM Ward). We carry out evaluation experiments to compare the accuracy of the proposed SVM models with that of the \(\varepsilon \)-SVR using Welch’s t-test and effect sizes. These results indicate that the methods SVM same # (1) and SVM Ward (3) can improve the accuracy of estimating the amount of effort in terms of the mean percentage of predictions that fall within 25 % of the actual value.

High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinical practice still relies on physiological, non-omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, we studied a mouse model of diet-induced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. We find that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue-specific manner—treatments that reverse the transcriptomic signatures of the disease in a particular tissue are associated with positive physiological effects in that tissue. Further, treatments that introduce large non-restorative gene expression alterations are associated with unfavorable physiological outcomes. These results provide a sound basis to in silico methods that rely on omic metrics for drug repurposing and drug discovery by searching for compounds that reverse a disease's omic signatures. Moreover, they highlight the need to develop drugs that restore the global cellular state to its healthy norm rather than rectify particular disease phenotypes.

This paper considers the problem of representative selection: choosing a subset of data points from a dataset that best represents its overall set of elements. This subset needs to inherently reflect the type of information contained in the entire set, while minimizing redundancy. For such purposes, clustering may seem like a natural approach. However, existing clustering methods are not ideally suited for representative selection, especially when dealing with non-metric data, where only a pairwise similarity measure exists. In this paper we propose $\delta$-medoids, a novel approach that can be viewed as an extension to the $k$-medoids algorithm and is specifically suited for sample representative selection from non-metric data. We empirically validate $\delta$-medoids in two domains, namely music analysis and motion analysis. We also show some theoretical bounds on the performance of $\delta$-medoids and the hardness of representative selection in general.