Fault detection is a key challenge in the management of complex systems. In the context of SparkCognition's efforts towards predictive maintenance in large scale industrial systems, this problem is often framed in terms of anomaly detection - identifying patterns of behavior in the data which deviate from normal. Patterns of normal behavior aren't captured simply in the coarse statistics of measured signals. Rather, the multivariate sequential pattern itself can be indicative of normal vs. abnormal behavior. For this reason, normal behavior modeling that relies on snapshots of the data without taking into account temporal relationships as they evolve would be lacking. However, common strategies for dealing with temporal dependence, such as Recurrent Neural Networks or attention mechanisms are oftentimes computationally expensive and difficult to train. In this paper, we propose a fast and efficient approach to anomaly detection and alert filtering based on sequential pattern similarities. In our empirical analysis section, we show how this approach can be leveraged for a variety of purposes involving anomaly detection on a large scale real-world industrial system. Subsequently, we test our approach on a publicly-available dataset in order to establish its general applicability and robustness compared to a state-of-the-art baseline. We also demonstrate an efficient way of optimizing the framework based on an alert recall objective function.

Current approaches to learning cooperative behaviors in multi-agent settings assume relatively restrictive settings. In standard fully cooperative multi-agent reinforcement learning, the learning algorithm controls \textit{all} agents in the scenario, while in ad hoc teamwork, the learning algorithm usually assumes control over only a $\textit{single}$ agent in the scenario. However, many cooperative settings in the real world are much less restrictive. For example, in an autonomous driving scenario, a company might train its cars with the same learning algorithm, yet once on the road, these cars must cooperate with cars from another company. Towards generalizing the class of scenarios that cooperative learning methods can address, we introduce $N$-agent ad hoc teamwork, in which a set of autonomous agents must interact and cooperate with dynamically varying numbers and types of teammates at evaluation time. This paper formalizes the problem, and proposes the $\textit{Policy Optimization with Agent Modelling}$ (POAM) algorithm. POAM is a policy gradient, multi-agent reinforcement learning approach to the NAHT problem, that enables adaptation to diverse teammate behaviors by learning representations of teammate behaviors. Empirical evaluation on StarCraft II tasks shows that POAM improves cooperative task returns compared to baseline approaches, and enables out-of-distribution generalization to unseen teammates.

Current approaches to multi-agent cooperation rely heavily on centralized mechanisms or explicit communication protocols to ensure convergence. This paper studies the problem of distributed multi-agent learning without resorting to centralized components or explicit communication. It examines the use of distribution matching to facilitate the coordination of independent agents. In the proposed scheme, each agent independently minimizes the distribution mismatch to the corresponding component of a target visitation distribution. The theoretical analysis shows that under certain conditions, each agent minimizing its individual distribution mismatch allows the convergence to the joint policy that generated the target distribution. Further, if the target distribution is from a joint policy that optimizes a cooperative task, the optimal policy for a combination of this task reward and the distribution matching reward is the same joint policy. This insight is used to formulate a practical algorithm (DM$^2$), in which each individual agent matches a target distribution derived from concurrently sampled trajectories from a joint expert policy. Experimental validation on the StarCraft domain shows that combining (1) a task reward, and (2) a distribution matching reward for expert demonstrations for the same task, allows agents to outperform a naive distributed baseline. Additional experiments probe the conditions under which expert demonstrations need to be sampled to obtain the learning benefits.

Computers have been used to analyze and create music since they were first introduced in the 1950s and 1960s. Beginning in the late 1990s, the rise of the Internet and large scale platforms for music recommendation and retrieval have made music an increasingly prevalent domain of machine learning and artificial intelligence research. While still nascent, several different approaches have been employed to tackle what may broadly be referred to as "musical intelligence." This article provides a definition of musical intelligence, introduces a taxonomy of its constituent components, and surveys the wide range of AI methods that can be, and have been, brought to bear in its pursuit, with a particular emphasis on machine learning methods.

Concept drift - a change, either sudden or gradual, in the underlying properties of data - is one of the most prevalent challenges to maintaining high-performing learned models over time in autonomous systems. In the face of concept drift, one can hope that the old model is sufficiently representative of the new data despite the concept drift, one can discard the old data and retrain a new model with (often limited) new data, or one can use transfer learning methods to combine the old data with the new to create an updated model. Which of these three options is chosen affects not only near-term decisions, but also future needs to transfer or retrain. In this paper, we thus model response to concept drift as a sequential decision making problem and formally frame it as a Markov Decision Process. Our reinforcement learning approach to the problem shows promising results on one synthetic and two real-world datasets.

My main research motivation is to develop complete autonomous agents that interact with people socially. For an agent to be social with respect to humans, it needs to be able to parse and process the multitude of aspects that comprise the human cultural experience. That in itself gives rise to many fascinating learning problems. I am interested in tackling these fundamental problems from an empirical as well as a theoretical perspective. Music, as a general target domain, serves as an excellent testbed for these research ideas. Musical skills---playing music (alone or in a group), analyzing music or composing it---all involve extremely advanced knowledge representation and problem solving tools. Creating "musical agents"---agents that can interact richly with people in the music domain---is a challenge that holds the potential of advancing social agents research, and contributing important and broadly applicable AI knowledge. This belief is fueled not just by my background in computer science and artificial intelligence, but also by my deep passion for music as well as my extensive musical training. One key aspect of musical intelligence which hasn’t been sufficiently studied is that of sequential decision-making. My thesis strives to answer the following question: How can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and multiagent interaction in the context of music.

In recent years, there has been growing interest in the study of automated playlist generation — music recommender systems that focus on modeling preferences over song sequences rather than on individual songs in isolation. This paper addresses this problem by learning personalized models on the fly of both song and transition preferences, uniquely tailored to each user’s musical tastes. Playlist recommender systems typically include two main components: i) a preference-learning component, and ii) a planning component for selecting the next song in the playlist sequence. While there has been much work on the former, very little work has been devoted to the latter. This paper bridges this gap by focusing on the planning aspect of playlist generation within the context of DJ-MC, our playlist recommendation application. This paper also introduces a new variant of playlist recommendation, which incorporates the notion of diversity and novelty directly into the reward model. We empirically demonstrate that the proposed planning approach significantly improves performance compared to the DJ-MC baseline in two playlist recommendation settings, increasing the usability of the framework in real world settings.

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.