Resilience in the Facility Location Problem: Theory and Practice

设施选址问题的弹性：理论与实践

• 批准号: 20K11947
• 负责人: Nicolas Schwind
• 金额: $ 2.75万
• 依托单位: National Institute of Advanced Industrial Science and Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2020
• 资助国家: 日本
• 起止时间: 2020-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20K11947/
• 关键词: Knowledge Representation; Robustness; Resilience; Iterated Change; Facility Location; Multiagent systems; Team Formation; Belief Change; Improvement

We have made significant progress in researching partial robustness in team formation and facility location. Our investigation involved comparing our findings with relevant works in coalition structure generation, set cover, and facility location. The research paper underwent two rounds of reviews and was successfully published in the journal of Autonomous Agents and Multi-Agent Systems.Furthermore, we developed software that generates artificial facility location instances, offering users flexibility in generating diverse instances with various parameters. The software includes solving algorithms and a visualization tool. Additionally, we created a benchmark set using the software, which will be utilized by the SAT competition community. The detailed generation protocol for this benchmark set was published in the proceedings of SAT Competition 2022: Solver and Benchmark Descriptions.We also conducted research on the theoretical and fundamental aspects of exogenous change and resilience, focusing on knowledge representation. Our findings were presented in a paper at KR'22, a prestigious international conference specializing in knowledge representation.Overall, we have achieved significant milestones, including publishing in a reputable journal, releasing comprehensive software, contributing to benchmark sets, and presenting research at a top-tier conference.

我们在团队编队和设施选址方面的部分稳健性研究取得了重大进展。我们的调查包括将我们的发现与联盟结构生成、场景覆盖和设施选址方面的相关工作进行比较。该研究论文经过了两轮评审，并成功发表在《自主代理和多代理系统》杂志上，此外，我们还开发了生成人工设施位置实例的软件，为用户提供了灵活地生成各种参数的实例。该软件包括求解算法和可视化工具。此外，我们使用该软件创建了一个基准集，将供SAT竞赛社区使用。该基准测试集的详细生成协议发表在《2022年SAT竞赛论文集：求解器和基准描述》中。我们还对外生变化和弹性的理论和基础方面进行了研究，重点是知识表示。我们的发现发表在KR‘22上的一篇论文中，KR’22是一个享有盛誉的国际会议，专门研究知识表示。总体而言，我们取得了重大的里程碑，包括在声誉良好的期刊上发表文章，发布全面的软件，为基准集做出贡献，以及在顶级会议上展示研究成果。

项目成果

Partial Robustness in Team Formation: Bridging the Gap between Robustness and Resilience

• DOI: 10.5555/3463952.3464086
• 发表时间: 2021
• 作者: Nicolas Schwind;Emir Demirovic;Katsumi Inoue;Jean-Marie Lagniez

Non-Prioritized Iterated Revision: Improvement via Incremental Belief Merging

非优先迭代修订：通过增量信念合并进行改进

• 发表时间: 2020
• 作者: Nicolas Schwind;Sebastien Konieczny
• 通讯作者: Sebastien Konieczny

Our generation, solving, vizualisation software

我们的生成、求解、可视化软件

Algorithms for partially robust team formation

• DOI: 10.1007/s10458-023-09608-7
• 发表时间: 2023-04
• 期刊: Autonomous Agents and Multi-Agent Systems
• 影响因子: 1.9
• 作者: Nicolas Schwind;Emir Demirovic;Katsumi Inoue;Jean-Marie Lagniez

On the Representation of Darwiche and Pearls Epistemic States for Iterated Belief Revision

关于迭代信念修正的 Darwiche 和 Pearls 认知状态的表示

• 发表时间: 2022
• 作者: Nicolas Schwind;Sebastien Konieczny;Ramon Pino Perez
• 通讯作者: Ramon Pino Perez