Agile Planning for Real-World Disaster Response

Wu, Feng; Ramchurn, Sarvapali D.; Jiang, Wenchao; Fischer, Joel E.; Rodden, Tom; Jennings, Nicholas R.

Abstract

We consider a setting where an agent-based planner instructs teams of human emergency responders to perform tasks in the real world. Due to uncertainty in the environment and the inability of the planner to consider all human preferences and all attributes of the real-world, humans may reject plans computed by the agent. A na¨ıve solution that replans given a rejection is inefficient and does not guarantee the new plan will be acceptable. Hence, we propose a new model re-planning problem using a Multi-agent Markov Decision Process that integrates potential rejections as part of the planning process and propose a novel algorithm to efficiently solve this new model. We empirically evaluate our algorithm and show that it outperforms current benchmarks. Our algorithm is also shown to perform better in pilot studies with real humans.

Citation

Wu, F., Ramchurn, S. D., Jiang, W., Fischer, J. E., Rodden, T., & Jennings, N. R. (2015). Agile Planning for Real-World Disaster Response. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (132-138)

• Conference Name: International Joint Conference on Artificial Intelligence (IJCAI-15)
• Conference Location: Buenos Aires, Argentina
• Start Date: Jul 25, 2015
• End Date: Jul 31, 2015
• Online Publication Date: Jul 25, 2015
• Publication Date: Jul 25, 2015
• Deposit Date: Jan 29, 2016
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 2015-July
• Pages: 132-138
• Book Title: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence
• ISBN: 9781577357384
• Public URL: https://nottingham-repository.worktribe.com/output/755965
• Publisher URL: https://dl.acm.org/doi/10.5555/2832249.2832268
• Additional Information: Published in: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence: Buenos Aires, Argentina, 25–31 July 2015. Palo Alto, Calif. : AAAI Press/International Joint Conferences on Artificial Intelligence, 2015. ISBN: 978-1-5773-5738-4, pp. 132-138