A significant number and range of challenges besetting sustainability can be traced to the actions and interactions of multiple autonomous agents (people mostly) and the entities they create (e.g., institutions, policies, social network) in the corresponding social-environmental systems (SES). To address these challenges, we need to understand decisions made and actions taken by agents, the outcomes of their actions, including the feedbacks on the corresponding agents and environment. The science of complex adaptive systems—CAS science—has a significant potential to handle such challenges. We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science, the generic features of CAS, and the key advances and challenges in modeling CAS. Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’ behaviors, detect SES structures, and formulate SES mechanisms.
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A Real-Time Rescheduling Algorithm for Multi-robot Plan Execution
One area of research in multi-agent path finding is to determine how replanning can be efficiently achieved in the case of agents being delayed during execution. One option is to reschedule the passing order of agents, i.e., the sequence in which agents visit the same location. In response, we propose Switchable-Edge Search (SES), an A*-style algorithm designed to find optimal passing orders. We prove the optimality of SES and evaluate its efficiency via simulations. The best variant of SES takes less than 1 second for small- and medium-sized problems and runs up to 4 times faster than baselines for large-sized problems.
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- Award ID(s):
- 2328671
- PAR ID:
- 10628095
- Publisher / Repository:
- AAAI
- Date Published:
- Journal Name:
- Proceedings of the International Conference on Automated Planning and Scheduling
- Volume:
- 34
- ISSN:
- 2334-0835
- Page Range / eLocation ID:
- 201 to 209
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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