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Titlebook: Algorithmic Decision Theory; 6th International Co Saša Pekeč,Kristen Brent Venable Conference proceedings 2019 Springer Nature Switzerland

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期刊全称Algorithmic Decision Theory
期刊简称6th International Co
影响因子2023Saša Pekeč,Kristen Brent Venable
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Algorithmic Decision Theory; 6th International Co Saša Pekeč,Kristen Brent Venable Conference proceedings 2019 Springer Nature Switzerland
影响因子This book constitutes the conference proceedings of the 6th International Conference on Algorithmic Decision Theory, ADT 2019, held in Durham, NC, USA, in October 2019.  The 10 full papers presented together with 7 short papers were carefully selected from 31 submissions. The papers focus on algorithmic decision theory broadly defined, seeking to bring together researchers and practitioners coming from diverse areas of computer science, economics and operations research in order to improve the theory and practice of modern decision support.
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Towards Characterizing the Deterministic Combinatorial Constrained Efficient Space,n that the valuation for the largest bundle is more than the budget for at least one of the players then the following are true. (a) VCG does not fulfill the basic properties of deterministic, dominant-strategy incentive compatible, individual rationality and Pareto optimality when the all-item bund
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On the Parameterized Complexity of Party Nominations, even on profiles that are both single-peaked and single-crossing. This strengthens previously established hardness results. We also show a polynomial time result for the related . problem on single-crossing elections.
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https://doi.org/10.1007/b102376ters (e.g., a weighted sum, an OWA aggregator, a Choquet integral) and that a (near-)optimal solution can be efficiently determined when preferences are precisely known. For the multi-objective traveling salesman problem, we provide numerical results obtained with different query generation strategi
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Combining Local Search and Elicitation for Multi-Objective Combinatorial Optimization,timization problems with imprecise preferences. We assume that the decision maker’s preferences over solutions can be represented by a parameterized scalarizing function but the parameters are initially not known. In our approach, the parameter imprecision is progressively reduced by iteratively ask
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Robustness of Approval-Based Multiwinner Voting Rules,e extent to which a committee can change after we add/remove/swap one approval, and we consider the computational complexity of deciding how many such operations are necessary to change the set of winning committees. We also consider the counting variants of our problems, which can be interpreted as
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