Resilience in the Facility Location Problem: Theory and Practice
| Project/Area Number |
20K11947
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 61030:Intelligent informatics-related
|
|---|
| Research Institution | National Institute of Advanced Industrial Science and Technology |
|---|
Principal Investigator |
Nicolas Schwind 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 主任研究員 (60646397)
|
|---|
| Project Period (FY) |
2020-04-01 – 2024-03-31
|
| Project Status |
Completed (Fiscal Year 2023)
|
| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2021: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
|
|---|
| Keywords | Knowledge Representation / Belief Change / Dynamic Systems / Iterated Change / Robustness / Resilience / Facility Location / Multiagent systems / Team Formation / Improvement |
|---|
| Outline of Research at the Start |
We will provide a solution to deploy a set of facilities on a populated map that is robust to natural disasters, that is, to ensure a certain quality of service in all phases of a disaster scenario. In particular, we will formalize the new resilience notion in the popular Coalition Formation framework, introduce algorithms, and design benchmarks based on real-world data. By the end of the project, we will make publicly available a software which, given a chosen populated map, finds a resilient facility deployment according to our notion and in efficient time.
|
| Outline of Annual Research Achievements |
In the past year, we've exceeded our project's main goal by exploring a crucial aspect of Artificial Intelligence: how to effectively integrate new information into formal systems, such as databases or multi-agent systems, within the context of systems resilience. This poses a significant challenge when the incoming data originates from diverse sources, potentially conflicting with the system's existing understanding. Yet, it's vital to integrate this information while adhering to rational principles. Over decades, scholars have scrutinized these principles, and our research has focused on two main areas.
Firstly, we've pinpointed limitations in the current framework of iterated change, specifically iterated belief revision, which governs how a system's state evolves with new information. To address this, we've introduced new rationality principles guiding how multiple change iterations affect the system. Additionally, we've proposed practical strategies for iterated revision that align with these principles. Secondly, we've delved into belief update, another form of system adaptation distinct from belief revision, and presented two credible models for it.
Our findings have been disseminated through publications and presentations at the 20th International Conference on Principles of Knowledge Representation and Reasoning (KR'23), a prestigious forum dedicated to the theoretical aspects of Artificial Intelligence and Knowledge Representation.
|
Report
(4 results)
Research Products
(11 results)
