70 research outputs found

    Machine-Learning-Powered Cyber-Physical Systems

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    In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety of services and applications across multiple domains. Machine learning processes and analyses such data to generate a feedback, which represents a status the environment is in. A feedback given to the user in order to make an informed decision is called an open-loop feedback. Thus, an open-loop CPS is characterized by the lack of an actuation directed at improving the system itself. A feedback used by the system itself to actuate a change aimed at optimizing the system itself is called a closed-loop feedback. Thus, a closed-loop CPS pairs feedback based on sensing data with an actuation that impacts the system directly. In this dissertation, we propose several applications in the context of CPS. We propose open-loop CPSs designed for the early prediction, diagnosis, and persistency detection of Bovine Respiratory Disease (BRD) in dairy calves, and for gait activity recognition in horses.These works use sensor data, such as pedometers and automated feeders, to perform valuable real-field data collection. Data are then processed by a mix of state-of-the-art approaches as well as novel techniques, before being fed to machine learning algorithms for classification, which informs the user on the status of their animals. Our work further evaluates a variety of trade-offs. In the context of BRD, we adopt optimization techniques to explore the trade-offs of using sensor data as opposed to manual examination performed by domain experts. Similarly, we carry out an extensive analysis on the cost-accuracy trade-offs, which farmers can adopt to make informed decisions on their barn investments. In the context of horse gait recognition we evaluate the benefits of lighter classifications algorithms to improve energy and storage usage, and their impact on classification accuracy. With respect to closed-loop CPS we proposes an incentive-based demand response approach for Heating Ventilation and Air Conditioning (HVAC) designed for peak load reduction in the context of smart grids. Specifically, our approach uses machine learning to process power data from smart thermostats deployed in user homes, along with their personal temperature preferences. Our machine learning models predict power savings due to thermostat changes, which are then plugged into our optimization problem that uses auction theory coupled with behavioral science. This framework selects the set of users who fulfill the power saving requirement, while minimizing financial incentives paid to the users, and, as a consequence, their discomfort. Our work on BRD has been published on IEEE DCOSS 2022 and Frontiers in Animal Science. Our work on gait recognition has been published on IEEE SMARTCOMP 2019 and Elsevier PMC 2020, and our work on energy management and energy prediction has been published on IEEE PerCom 2022 and IEEE SMARTCOMP 2022. Several other works are under submission when this thesis was written, and are included in this document as well

    Environmental analysis for application layer networks

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    Die zunehmende Vernetzung von Rechners über das Internet lies die Vision von Application Layer Netzwerken aufkommen. Sie umfassen Overlay Netzwerke wie beispielsweise Peer-to-Peer Netzwerke und Grid Infrastrukturen unter Verwendung des TCP/IP Protokolls. Ihre gemeinsame Eigenschaft ist die redundante, verteilte Bereitstellung und der Zugang zu Daten-, Rechen- und Anwendungsdiensten, während sie die Heterogenität der Infrastruktur vor dem Nutzer verbergen. In dieser Arbeit werden die Anforderungen, die diese Netzwerke an ökonomische Allokationsmechanismen stellen, untersucht. Die Analyse erfolgt anhand eines Marktanalyseprozesses für einen zentralen Auktionsmechanismus und einen katallaktischen Markt

    Sequential Decision Making with Strategic Agents and Limited Feedback

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    Sequential decision-making is a natural model for machine learning applications where the learner must make online decisions in real time and simultaneously learn from the sequential data to make better decisions in the future. Classical work has focused on variants of the problem based on the data distribution being either stochastic or adversarial, or based on the feedback available to the learner’s decisions which could be either partial or complete. With the rapid rise of large online markets, sequential learning methods have increasingly been deployed in complex multi-agent systems where agents may behave strategically to optimize for their own personal objectives. This has added a new dimension to the sequential decision-making problem where the learner must account for the strategic behavior of the agents it is learning from who might want to steer its future decisions in their favor. This thesis aims to design effective online decision-making algorithms from the point of view of the system designers aiming to learn in environments with strategic agents and limited feedback and the strategic agents seeking to optimize personal objectives. In the first part of the thesis, we focus on repeated auctions and design mechanisms where the auctioneer can effectively learn in the presence of strategic bidders, and conversely, address how agents can bid in repeated auctions or use data-poisoning attacks to maximize their own objectives. In the second part, we consider an online learning setting where feedback about the learner’s decisions is expensive to obtain. We introduce an online learning algorithm inspired by techniques from active learning that can fast forward a small fraction of more informative examples ahead in the queue. This allows the learner to obtain the same performance as the optimal online algorithm but only by querying feedback on a very small fraction of points. Finally, in the third part of the thesis, we consider a new learning objective for stochastic multi-arm bandits that promotes merit-based fairness in opportunity for individuals and groups.Ph.D

    Learning and Robustness With Applications To Mechanism Design

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    The design of economic mechanisms, especially auctions, is an increasingly important part of the modern economy. A particularly important property for a mechanism is strategyproofness -- the mechanism must be robust to strategic manipulations so that the participants in the mechanism have no incentive to lie. Yet in the important case when the mechanism designer's goal is to maximize their own revenue, the design of optimal strategyproof mechanisms has proved immensely difficult, with very little progress after decades of research. Recently, to escape this impasse, a number of works have parameterized auction mechanisms as deep neural networks, and used gradient descent to successfully learn approximately optimal and approximately strategyproof mechanisms. We present several improvements on these techniques. When an auction mechanism is represented as a neural network mapping bids from outcomes, strategyproofness can be thought of as a type of adversarial robustness. Making this connection explicit, we design a modified architecture for learning auctions which is amenable to integer-programming-based certification techniques from the adversarial robustness literature. Existing baselines are empirically strategyproof, but with no way to be certain how strong that guarantee really is. By contrast, we are able to provide perfectly tight bounds on the degree to which strategyproofness is violated at any given point. Existing neural networks for auctions learn to maximize revenue subject to strategyproofness. Yet in many auctions, fairness is also an important concern -- in particular, fairness with respect to the items in the auction, which may represent, for instance, ad impressions for different protected demographic groups. With our new architecture, ProportionNet, we impose fairness constraints in addition to the strategyproofness constraints, and find approximately fair, approximately optimal mechanisms which outperform baselines. With PreferenceNet, we extend this approach to notions of fairness that are learned from possibly vague human preferences. Existing network architectures can represent additive and unit-demand auctions, but are unable to imposing more complex exactly-k constraints on the allocations made to the bidders. By using the Sinkhorn algorithm to add differentiable matching constraints, we produce a network which can represent valid allocations in such settings. Finally, we present a new auction architecture which is a differentiable version of affine maximizer auctions, modified to offer lotteries in order to potentially increase revenue. This architecture is always perfectly strategyproof (avoiding the Lagrangian-based constrained optimization of RegretNet) -- to achieve this goal, however, we need to accept that we cannot in general represent the optimal auction

    Electricity Industry Competition and Market Power with High Renewable Penetrations

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    Globally, energy systems are expected to undergo a complete transition from fossil- fuelled generation to renewable energy in the coming decades, with a majority of energy supplied by wind and solar in many countries. In much of the developed world, this transition will take place in the context of restructured electricity markets. This thesis examines whether electricity markets, which are intended to be the key drivers of electricity industry operation and investment, are suitably designed and implemented for transitioning to high penetrations of renewable energy. Of particular interest is the role of competition in delivering efficient market outcomes, the potential for exertion of market power in high-penetration renewable energy scenarios, and whether current auction designs to incentivise efficient behaviour will be effective in the context of energy delivered at near-zero marginal cost. Previous work on electricity market competition in Australia has focused on measuring market concentration, a commonly used indicator of competitiveness, on short-term time horizons, based on historical data. However, competitiveness in Australia’s National Electricity Market (NEM) in the long term has not been assessed, nor how it might change as a result of the transition to high penetrations of variable renewable energy (VRE). This may be due in part to lack of suitable measures of competition in markets with multiple interconnected regions, but also the theory and evidence around VRE bidding patterns now and into the future has not yet been confirmed. Assessing competitiveness of future markets requires new methods for modelling and assessing potential market dynamics that affect market power. While capacity expansion modelling has been used for understanding the future technical and economic performance of electricity systems with different generation technologies, there have been very few attempts to relate these models back to the concepts of competition and market concentration. Machine learning techniques may also have the potential to provide new insights into the strategic behaviour of participants in future energy systems and have been used for modelling and solving many other complex multi-agent interactions, but to date a straightforward method for applying modern machine learning techniques to models of competitive electricity markets has not been proposed. Furthermore, significant changes that are under consideration to facilitate the energy transition, such as the introduction of a new two-sided market design in the NEM that would require all demand-side participants to submit bids, have not been considered in modelling to date. This thesis aims to investigate competition and market power in restructured electricity markets as well as their role in the clean energy transition. It investigates whether the Australian NEM has been and will continue to be a competitive market through the transition to renewable energy and how renewable generators participate in electricity auctions now and into the future. Additionally, it examines the way new tools and frameworks might further understandings of incentives and behaviour to enable more efficient and stable market designs. In order to establish a theoretical base and explore what causes market mechanism failure, a literature review and case study are undertaken into episodes of the exercise of market power globally, with a specific focus on the Californian electricity crisis. To establish how well market mechanisms are currently working, a range of competition metrics are applied to historical datasets in order to study the level of competitiveness of the Australian National Electricity Market. This leads to new answers to the question of whether the NEM is currently a competitive market, showing that current market concentration indicators provide conflicting results depending on how they are applied. A new measure of competition is provided which demonstrates that most regions are generally competitive, but some, such as Queensland, have notable periods of constraint. In order to determine how the transition to renewables might impact competition in the NEM, new indicators of competitiveness are also applied to simulations of future high-penetration renewable energy scenarios. These analyses demonstrate that swings between surplus and constraint can lead to an increase in the frequency of opportunities to exercise market power. This is an important result that shows how high-penetration renewables may significantly disrupt the function of wholesale electricity spot markets. To understand both the underlying incentives acting on renewable generators in the NEM and the current bidding strategies of these generators a case study of these generators in the NEM is undertaken. It is seen that these participants generally offer energy at or below $0/MWh, but are occasionally seen to bid at very high prices, possibly in an attempt to push up the spot price. Following this analysis, in order to examine what strategic incentives might be present in future high-penetration renewable energy grids, new equilibria for near-zero marginal cost generators are proposed. Following on from these investigations, the performance of a two-sided market in a 99% renewable energy grid is explored. In a two-sided market, flexible demand-side participants would be required to enter bids into the wholesale market. Based on forecasts of flexible demand response and renewable energy performance in a 99% renewable energy scenario, this modelling showed that demand response was, counterintuitively, less likely to be present in a two-sided market; additionally, the two-sided market was seen to mitigate the impacts of the exercise of market power because the more elastic supply curve placed upper limits on strategic generator offers. In order to develop a new modelling framework for renewable bidding behaviour in recognition of the difficulties in modelling competitive equilibria for future high- penetration renewable electricity market conditions, a market simulator is developed for the OpenAI platform that can be used to train deep learning models of electricity market bidding. Such models may be extremely useful in the context of the transition to high-penetration renewables, because competitive dynamics could be accurately predicted and understood before new capacity is built and operated. There are several key contributions of this work; it presents a new method for calculating and estimating levels of competition in electricity markets such as the NEM, which are comprised of multiple regions with constrained interconnectors, provides and applies a new methodology for exploring thresholds of competitiveness in simulations of future energy systems, develops the first long-term exploration of renewable bidding behaviour in Australia’s NEM, gives a new tool for running market behaviour experiments with emerging AI tools, and provides an early analysis of the impact of implementing a two-sided market mechanism, as proposed by Australia’s Energy Security Board. Together, these contributions may help to significantly enhance current understandings of the opportunities and challenges associated with transitioning to high-penetration renewable energy within a wholesale electricity market

    Why Do Pricing Rules Matter? Electricity Market Design with Electric Vehicle Participants

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    The energy transition, a process in which fossil fuels are being replaced by cleaner sources of energy, comes with many challenges. The intrinsic uncertainty associated with renewable energy sources has led to a search for complementary technologies to tackle those issues. In recent years, the use of electric vehicles (EVs) has been studied as an alternative for storage, leading to a much more complex market structure. Small participants are now willing to provide energy, helping to keep the desired balance of supply and demand. In this paper, we analyse the electricity spot market, providing a model where EVs decide to participate depending on the underlying conditions. We study pricing rules adapted from versions currently in use in electricity markets, and focus on two of them for our experimental settings: integer programming (IP) and extended locational marginal (ELM) pricing. We particularly pay attention to the properties those prices might satisfy, and numerically test them under some scenarios representing different levels of participation of EVs and an active demand side. Our results suggest that IP pricing generally derives larger individual uplift payments and further produces public prices that are not well aligned with the final payments of market participants, leading to distortions in the market

    Social Welfare

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    "Social Welfare" offers, for the first time, a wide-ranging, internationally-focused selection of cutting-edge work from leading academics. Its interdisciplinary approach and comparative perspective promote examination of the most pressing social welfare issues of the day. The book aims to clarify some of the ambiguity around the term, discuss the pros and cons of privatization, present a range of social welfare paradoxes and innovations, and establish a clear set of economic frameworks with which to understand the conditions under which the change in social welfare can be obtained

    Can Upward Brand Extensions be an Opportunity for Marketing Managers During the Covid-19 Pandemic and Beyond?

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    Early COVID-19 research has guided current managerial practice by introducing more products across different product categories as consumers tried to avoid perceived health risks from food shortages, i.e. horizontal brand extensions. For example, Leon, a fast-food restaurant in the UK, introduced a new range of ready meal products. However, when the food supply stabilised, availability may no longer be a concern for consumers. Instead, job losses could be a driver of higher perceived financial risks. Meanwhile, it remains unknown whether the perceived health or financial risks play a more significant role on consumers’ consumptions. Our preliminary survey shows perceived health risks outperform perceived financial risks to positively influence purchase intention during COVID-19. We suggest such a result indicates an opportunity for marketers to consider introducing premium priced products, i.e. upward brand extensions. The risk-as�feelings and signalling theories were used to explain consumer choice under risk may adopt affective heuristic processing, using minimal cognitive efforts to evaluate products. Based on this, consumers are likely to be affected by the salient high-quality and reliable product cue of upward extension signalled by its premium price level, which may attract consumers to purchase when they have high perceived health risks associated with COVID-19. Addressing this, a series of experimental studies confirm that upward brand extensions (versus normal new product introductions) can positively moderate the positive effect between perceived health risks associated with COVID-19 and purchase intention. Such an effect can be mediated by affective heuristic information processing. The results contribute to emergent COVID-19 literature and managerial practice during the pandemic but could also inform post-pandemic thinking around vertical brand extensions

    2021 The Year in Review

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    Meeting proceedings of a seminar by the same name, held December 9-10, 2021

    Multi-Agent Systems

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    A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains
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