256 research outputs found

    Learning partner selection rules that sustain cooperation in social dilemmas with the option of opting out

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    We study populations of self-interested agents playing a 2-person repeated Prisoner’s Dilemma game, with each player having the option of opting out of the interaction and choosing to be randomly assigned to another partner instead. The partner selection component makes these games akin to random matching, where defection is known to take over the entire population. Results in the literature have shown that, when forcing agents to obey a set partner selection rule known as Out-for-Tat, where defectors are systematically being broken ties with, cooperation can be sustained in the long run. In this paper, we remove this assumption and study agents that learn both action- and partner-selection strategies. Through multiagent reinforcement learning, we show that cooperation can be sustained without forcing agents to play predetermined strategies. Our simulations show that agents are capable of learning in-game strategies by themselves, such as Tit-for-Tat. What is more, they are also able to simultaneously discover cooperation-sustaining partner selection rules, notably Out-for-Tat, as well as other new rules that make cooperation prevail

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

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    This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023

    Protecting Micro-Data Privacy: The Moment-Based Density Estimation Method and its Application

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    Privacy concerns pertaining to the release of confidential micro-level information are increasingly relevant to organisations and institutions. Controlling the dissemination of disclosure-prone micro-data by means of suppression, aggregation and perturbation techniques often entails different levels of effectiveness and drawbacks depending on the context and properties of the data. In this dissertation, we briefly review existing disclosure control methods for microdata and undertake a study demonstrating the applicability of micro-data methods to proportion data. This is achieved by using the sample size efficiency related to a simple hypothesis test for a fixed significance level and power, as a measure of statistical utility. We compare a query-based differential privacy mechanism to the multiplicative noise method for disclosure control and demonstrate that with the correct specification of noise parameters, the multiplicative noise method, which is a micro-data based method, achieves similar disclosure protection properties with reduced statistical efficiency costs

    2023-2024 academic bulletin & course catalog

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    University of South Carolina Aiken publishes a catalog with information about the university, student life, undergraduate and graduate academic programs, and faculty and staff listings

    A matheuristic for customized multi-level multi-criteria university timetabling

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    Course timetables are the organizational foundation of a university’s educational program. While students and lecturers perceive timetable quality individually according to their preferences, there are also collective criteria derived normatively such as balanced workloads or idle time avoidance. A recent challenge and opportunity in curriculum-based timetabling consists of customizing timetables with respect to individual student preferences and with respect to integrating online courses as part of modern course programs or in reaction to flexibility requirements as posed in pandemic situations. Curricula consisting of (large) lectures and (small) tutorials further open the possibility for optimizing not only the lecture and tutorial plan for all students but also the assignments of individual students to tutorial slots. In this paper, we develop a multi-level planning process for university timetabling: On the tactical level, a lecture and tutorial plan is determined for a set of study programs; on the operational level, individual timetables are generated for each student interlacing the lecture plan through a selection of tutorials from the tutorial plan favoring individual preferences. We utilize this mathematical-programming-based planning process as part of a matheuristic which implements a genetic algorithm in order to improve lecture plans, tutorial plans, and individual timetables so as to find an overall university program with well-balanced timetable performance criteria. Since the evaluation of the fitness function amounts to invoking the entire planning process, we additionally provide a proxy in the form of an artificial neural network metamodel. Computational results exhibit the procedure’s capability of generating high quality schedules

    University of Windsor Undergraduate Calendar 2023 Spring

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    https://scholar.uwindsor.ca/universitywindsorundergraduatecalendars/1023/thumbnail.jp

    Computing the Fréchet distance between uncertain curves in one dimension.

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    We consider the problem of computing the Fréchet distance between two curves for which the exact locations of the vertices are unknown. Each vertex may be placed in a given uncertainty region for that vertex, and the objective is to place vertices so as to minimise the Fréchet distance. This problem was recently shown to be NP-hard in 2D, and it is unclear how to compute an optimal vertex placement at all. We present the first general algorithmic framework for this problem. We prove that it results in a polynomial-time algorithm for curves in 1D with intervals as uncertainty regions. In contrast, we show that the problem is NP-hard in 1D in the case that vertices are placed to maximise the Fréchet distance. We also study the weak Fréchet distance between uncertain curves. While finding the optimal placement of vertices seems more difficult than the regular Fréchet distance—and indeed we can easily prove that the problem is NP-hard in 2D—the optimal placement of vertices in 1D can be computed in polynomial time. Finally, we investigate the discrete weak Fréchet distance, for which, somewhat surprisingly, the problem is NP-hard already in 1D

    University of Windsor Undergraduate Calendar 2023 Winter

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    https://scholar.uwindsor.ca/universitywindsorundergraduatecalendars/1020/thumbnail.jp
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