249 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Mean-field games of speedy information access with observation costs

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    We investigate a mean-field game (MFG) in which agents can exercise control actions that affect their speed of access to information. The agents can dynamically decide to receive observations with less delay by paying higher observation costs. Agents seek to exploit their active information gathering by making further decisions to influence their state dynamics to maximize rewards. In the mean field equilibrium, each generic agent solves individually a partially observed Markov decision problem in which the way partial observations are obtained is itself also subject of dynamic control actions by the agent. Based on a finite characterisation of the agents' belief states, we show how the mean field game with controlled costly information access can be formulated as an equivalent standard mean field game on a suitably augmented but finite state space.We prove that with sufficient entropy regularisation, a fixed point iteration converges to the unique MFG equilibrium and yields an approximate ϵ\epsilon-Nash equilibrium for a large but finite population size. We illustrate our MFG by an example from epidemiology, where medical testing results at different speeds and costs can be chosen by the agents.Comment: 33 pages, 4 figure

    Online Algorithms with Randomly Infused Advice

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    We introduce a novel method for the rigorous quantitative evaluation of online algorithms that relaxes the "radical worst-case" perspective of classic competitive analysis. In contrast to prior work, our method, referred to as randomly infused advice (RIA), does not make any assumptions about the input sequence and does not rely on the development of designated online algorithms. Rather, it can be applied to existing online randomized algorithms, introducing a means to evaluate their performance in scenarios that lie outside the radical worst-case regime. More concretely, an online algorithm ALG with RIA benefits from pieces of advice generated by an omniscient but not entirely reliable oracle. The crux of the new method is that the advice is provided to ALG by writing it into the buffer ? from which ALG normally reads its random bits, hence allowing us to augment it through a very simple and non-intrusive interface. The (un)reliability of the oracle is captured via a parameter 0 ? ? ? 1 that determines the probability (per round) that the advice is successfully infused by the oracle; if the advice is not infused, which occurs with probability 1 - ?, then the buffer ? contains fresh random bits (as in the classic online setting). The applicability of the new RIA method is demonstrated by applying it to three extensively studied online problems: paging, uniform metrical task systems, and online set cover. For these problems, we establish new upper bounds on the competitive ratio of classic online algorithms that improve as the infusion parameter ? increases. These are complemented with (often tight) lower bounds on the competitive ratio of online algorithms with RIA for the three problems

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Online Scheduling with Predictions

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    Online scheduling is the process of allocating resources to tasks to achieve objectives with uncertain information about future conditions or task characteristics. This thesis presents a new online scheduling framework named online scheduling with predictions. The framework uses predictions about unknowns to manage uncertainty in decision-making. It considers that the predictions may be imperfect and include errors, surpassing the traditional assumptions of either complete information in online clairvoyant scheduling or zero information in online non-clairvoyant scheduling. The goal is to create algorithms with predictions that perform better with quality predictions while having bounded performance with poor predictions. The framework includes metrics such as consistency, robustness, and smoothness to evaluate algorithm performance. We prove the fundamental theorems that give tight lower bounds for these metrics. We apply the framework to central scheduling problems and cyber-physical system applications, including minimizing makespan in uniform machine scheduling with job size predictions, minimizing mean response time in single and parallel identical machine scheduling with job size predictions, and maximizing energy output in pulsed power load scheduling with normal load predictions. Analysis and simulations show that this framework outperforms state-of-the-art methods by leveraging predictions

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    Toward an Analysis of the Abductive Moral Argument for God’s Existence: Assessing the Evidential Quality of Moral Phenomena and the Evidential Virtuosity of Christian Theological Models

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    The moral argument for God’s existence is perhaps the oldest and most salient of the arguments from natural theology. In contemporary literature, there has been a focus on the abductive version of the moral argument. Although the mode of reasoning, abduction, has been articulated, there has not been a robust articulation of the individual components of the argument. Such an articulation would include the data quality of moral phenomena, the theoretical virtuosity of theological models that explain the moral phenomena, and how both contribute to the likelihood of moral arguments. The goal of this paper is to provide such an articulation. Our method is to catalog the phenomena, sort them by their location on the emergent hierarchy of sciences, then describe how the ecumenical Christian theological model exemplifies evidential virtues in explaining them. Our results show that moral arguments are neither of the highest or lowest quality yet can be assented to on a principled level of investigation, especially given existential considerations

    ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals

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    A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices.Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses.Comment: 11 pages, 9 figure
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