992 research outputs found

    Right Place, Right Time:Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty

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    For many multi-robot problems, tasks are announced during execution, where task announcement times and locations are uncertain. To synthesise multi-robot behaviour that is robust to early announcements and unexpected delays, multi-robot task allocation methods must explicitly model the stochastic processes that govern task announcement. In this paper, we model task announcement using continuous-time Markov chains which predict when and where tasks will be announced. We then present a task allocation framework which uses the continuous-time Markov chains to allocate tasks proactively, such that robots are near or at the task location upon its announcement. Our method seeks to minimise the expected total waiting duration for each task, i.e. the duration between task announcement and a robot beginning to service the task. Our framework can be applied to any multi-robot task allocation problem where robots complete spatiotemporal tasks which are announced stochastically. We demonstrate the efficacy of our approach in simulation, where we outperform baselines which do not allocate tasks proactively, or do not fully exploit our task announcement models

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    The Viability of Domain Constrained Coalition Formation for Robotic Collectives

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    Applications, such as military and disaster response, can benefit from robotic collectives' ability to perform multiple cooperative tasks (e.g., surveillance, damage assessments) efficiently across a large spatial area. Coalition formation algorithms can potentially facilitate collective robots' assignment to appropriate task teams; however, most coalition formation algorithms were designed for smaller multiple robot systems (i.e., 2-50 robots). Collectives' scale and domain-relevant constraints (i.e., distribution, near real-time, minimal communication) make coalition formation more challenging. This manuscript identifies the challenges inherent to designing coalition formation algorithms for very large collectives (e.g., 1000 robots). A survey of multiple robot coalition formation algorithms finds that most are unable to transfer directly to collectives, due to the identified system differences; however, auctions and hedonic games may be the most transferable. A simulation-based evaluation of three auction and hedonic game algorithms, applied to homogeneous and heterogeneous collectives, demonstrates that there are collective compositions for which no existing algorithm is viable; however, the experimental results and literature survey suggest paths forward.Comment: 46 pages, 9 figures, Swarm Intelligence (under review

    Price formation in Local Electricity Markets

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    Get Rich and Die Trying: Capitalism, Its Repetitions, and the Financial Plot

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    Most people want to be rich, and the reasons why usually do not require exposition. Despite gospel warnings about the difficulties of the wealthy entering paradise, multitudes clamor for the possibility of facing this dilemma firsthand. Tales from antiquity and mythologies utilize recognizable archetypes such as the profligate spender or stubborn miser that are still employed as rote moral instruction today. In one sense, exchangeability between positions of rich and poor is a staple of social storytelling because of its universal mutual intelligibility across time and place. Modern readers can likely identify descriptors and coding of rich and poor, despite the stark difference in access to economic resources enjoyed today. Something timeless or universal exists at the core of why identifications of rich and poor retain saliency across monumental shifts in political economy. I describe this creature of fiction as the financial plot. In this project, I show how this creature populates fiction of all stripes in complementary, typically below the surface or in an auxiliary position. I trace the financial plot through the primary texts of Joseph de la Vega’s Confusion of Confusions (1688), Charles Dickens’s Bleak House (1853), Anthony Trollope’s The Way We Live Now (1875), Joseph Conrad’s Chance (1913), and conclude with Theodore Dreiser’s The Financier (1912). Superficial assertions that the resolution of a financial plot is merely a holdover or vestigial handmaiden of morality are easily grasped, but to stop there would be to ignore of how the characters and economic conditions reflect larger attitudes and theorizations of political economy over time. By adding a second layer of analysis using economic history and historicized financial practices of Adam Smith, Karl Marx, Thorstein Veblen, and Joseph Schumpeter, the financial plot unlocks a new way of tracing the development and evolution of economic ideas and the larger structural changes brought about by the infusion and identification of market principles. Beneath the surface, the financial plot can reveal much more about the historical conditions of a text than its prevailing use as a supplemental or derivative element of characterization. By drawing upon economic and psychoanalytic insights, I theorize the financial plot’s ubiquity across time and political order in terms of the effect of death drive’s compulsion towards repetition amplified by a contemporary social circuit

    Constitutions of Value

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    Gathering an interdisciplinary range of cutting-edge scholars, this book addresses legal constitutions of value. Global value production and transnational value practices that rely on exploitation and extraction have left us with toxic commons and a damaged planet. Against this situation, the book examines law’s fundamental role in institutions of value production and valuation. Utilising pathbreaking theoretical approaches, it problematizes mainstream efforts to redeem institutions of value production by recoupling them with progressive values. Aiming beyond radical critique, the book opens up the possibility of imagining and enacting new and different value practices. This wide-ranging and accessible book will appeal to international lawyers, socio-legal scholars, those working at the intersections of law and economy and others, in politics, economics, environmental studies and elsewhere, who are concerned with rethinking our current ideas of what has value, what does not, and whether and how value may be revalued

    An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards

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    In many domains such as transportation and logistics, search and rescue, or cooperative surveillance, tasks are pending to be allocated with the consideration of possible execution uncertainties. Existing task coordination algorithms either ignore the stochastic process or suffer from the computational intensity. Taking advantage of the weakly coupled feature of the problem and the opportunity for coordination in advance, we propose a decentralized auction-based coordination strategy using a newly formulated score function which is generated by forming the problem into task-constrained Markov decision processes (MDPs). The proposed method guarantees convergence and at least 50% optimality in the premise of a submodular reward function. Furthermore, for the implementation on large-scale applications, an approximate variant of the proposed method, namely Deep Auction, is also suggested with the use of neural networks, which is evasive of the troublesome for constructing MDPs. Inspired by the well-known actor-critic architecture, two Transformers are used to map observations to action probabilities and cumulative rewards respectively. Finally, we demonstrate the performance of the two proposed approaches in the context of drone deliveries, where the stochastic planning for the drone league is cast into a stochastic price-collecting Vehicle Routing Problem (VRP) with time windows. Simulation results are compared with state-of-the-art methods in terms of solution quality, planning efficiency and scalability.Comment: 17 pages, 5 figure

    A review of task allocation methods for UAVs

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    Unmanned aerial vehicles, can offer solutions to a lot of problems, making it crucial to research more and improve the task allocation methods used. In this survey, the main approaches used for task allocation in applications involving UAVs are presented as well as the most common applications of UAVs that require the application of task allocation methods. They are followed by the categories of the task allocation algorithms used, with the main focus being on more recent works. Our analysis of these methods focuses primarily on their complexity, optimality, and scalability. Additionally, the communication schemes commonly utilized are presented, as well as the impact of uncertainty on task allocation of UAVs. Finally, these methods are compared based on the aforementioned criteria, suggesting the most promising approaches

    Dual Auction Mechanism for Transaction Forwarding and Validation in Complex Wireless Blockchain Network

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    In traditional blockchain networks, transaction fees are only allocated to full nodes (i.e., miners) regardless of the contribution of forwarding behaviors of light nodes. However, the lack of forwarding incentive reduces the willingness of light nodes to relay transactions, especially in the energy-constrained Mobile Ad Hoc Network (MANET). This paper proposes a novel dual auction mechanism to allocate transaction fees for forwarding and validation behaviors in the wireless blockchain network. The dual auction mechanism consists of two auction models: the forwarding auction and the validation auction. In the forwarding auction, forwarding nodes use Generalized First Price (GFP) auction to choose transactions to forward. Besides, forwarding nodes adjust the forwarding probability through a no-regret algorithm to improve efficiency. In the validation auction, full nodes select transactions using Vickrey-Clarke-Grove (VCG) mechanism to construct the block. We prove that the designed dual auction mechanism is Incentive Compatibility (IC), Individual Rationality (IR), and Computational Efficiency (CE). Especially, we derive the upper bound of the social welfare difference between the social optimal auction and our proposed one. Extensive simulation results demonstrate that the proposed dual auction mechanism decreases energy and spectrum resource consumption and effectively improves social welfare without sacrificing the throughput and the security of the wireless blockchain network

    Peer-to-Peer Energy Trading in Smart Residential Environment with User Behavioral Modeling

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    Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid. Trading energy among users in a decentralized fashion has been referred to as Peer- to-Peer (P2P) Energy Trading, which has attracted significant attention from the research and industry communities in recent times. However, previous research has mostly focused on engineering aspects of P2P energy trading systems, often neglecting the central role of users in such systems. P2P trading mechanisms require active participation from users to decide factors such as selling prices, storing versus trading energy, and selection of energy sources among others. The complexity of these tasks, paired with the limited cognitive and time capabilities of human users, can result sub-optimal decisions or even abandonment of such systems if performance is not satisfactory. Therefore, it is of paramount importance for P2P energy trading systems to incorporate user behavioral modeling that captures users’ individual trading behaviors, preferences, and perceived utility in a realistic and accurate manner. Often, such user behavioral models are not known a priori in real-world settings, and therefore need to be learned online as the P2P system is operating. In this thesis, we design novel algorithms for P2P energy trading. By exploiting a variety of statistical, algorithmic, machine learning, and behavioral economics tools, we propose solutions that are able to jointly optimize the system performance while taking into account and learning realistic model of user behavior. The results in this dissertation has been published in IEEE Transactions on Green Communications and Networking 2021, Proceedings of IEEE Global Communication Conference 2022, Proceedings of IEEE Conference on Pervasive Computing and Communications 2023 and ACM Transactions on Evolutionary Learning and Optimization 2023
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