DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 박진규 | - |
dc.contributor.author | Park, Heemang | - |
dc.contributor.author | 박희망 | - |
dc.date.accessioned | 2024-07-30T19:31:02Z | - |
dc.date.available | 2024-07-30T19:31:02Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1096689&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321472 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2024.2,[iv, 27 p. :] | - |
dc.description.abstract | In this work, we propose a novel method, NLNS-MASPF, to solve the Multi-Agent Scheduling and Pathfinding (MASPF) problem. The problem exhibits a bi-level structure, consisting of High-level Scheduling and Low-level Pathfinding. Our method applies a graph neural network in the high-level scheduling process and utilizes a MAPF solver with a schedule segmenting technique in the low-level pathfinding process. Through these approaches, NLNS-MASPF has experimentally demonstrated superior performance compared to the previous state-of-the-art MASPF algorithm, LNS-PBS, in solving the MASPF problem. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 스케줄링▼a다중 에이전트 경로 탐색▼a그래프 인공 신경망▼a기계 학습 | - |
dc.subject | Scheduling▼aMulti-agent path finding▼aGraph neural networks▼aMachine learning | - |
dc.title | NLNS-MASPF for multi-agent scheduling and path finding | - |
dc.title.alternative | 다중 에이전트 스케줄링 및 경로 탐색 기법 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :산업및시스템공학과, | - |
dc.contributor.alternativeauthor | Park, Jinkyoo | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.