Agile Planning for Real-World Disaster Response

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¨?ve 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.

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Identifiers

ORCID for Sarvapali Ramchurn: orcid.org/0000-0001-9686-4302

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