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Agile planning for real-world disaster response

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


Feng Wu

Sarvapali D. Ramchurn

Wenchao Jiang

Professor of Computer Science

Nicholas R. Jennings


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.

Publication Date Jul 31, 2015
Peer Reviewed Peer Reviewed
APA6 Citation Wu, F., Ramchurn, S. D., Jiang, W., Fischer, J. E., Rodden, T., & Jennings, N. R. (2015). Agile planning for real-world disaster response
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Copyright Statement Copyright information regarding this work can be found at the following address:
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