Lagrangian relaxation for large-scale multi-agent planning

• Main Authors: GORDON, Geoffrey J., VARAKANTHAM, Pradeep, YEOH, William, LAU, Hoong Chuin, ARAVAMUDHAN, Ajay S., CHENG, Shih-Fen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Gradient Descent; Lagrangian Relaxation; Multi-Agent Systems; Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4364; https://ink.library.smu.edu.sg/context/sis_research/article/5367/viewcontent/gordon_etal_lagrangian_relaxation_IAT.pdf
• Institution: Singapore Management University

Multi-agent planning is a well-studied problem with various applications including disaster rescue, urban transportation and logistics, both for autonomous agents and for decision support to humans. Due to computational constraints, existing research typically focuses on one of two scenarios: unstru...