RAPID: Resilient Automated Planning for Intelligent Disaster Response

An efficient disaster response plan is necessary for affected areas to recover quickly and with fewer fatalities. Automated planning is an area of artificial intelligence (AI) that finds a plan efficiently, considering relevant aspects of a problem. This work advances the field of automated planning for disaster response by offering a systematic and computationally effective framework for organizing resilience efforts during disasters. We formalize a domain using the planning domain definition language (PDDL) to use automated planning in various disaster resilience scenarios. Our proposed domain includes important activities like evacuation, rescue, medical support, and resource distribution. The domain allows prioritization of affected areas and considers various constraints such as the accessibility of a location and the capacity of different vehicles. The domain uses elegant PDDL components, for example, quantified preconditions and conditional effects, to incorporate those constraints. This domain is flexible to apply in various types of crises, such as floods and cyclones. We found plans with up to 279 actions for various instances of our domain with differing levels of complexity within a time limit of 30 minutes using a numeric planner, which explored over 1 million states to find the plan. A numeric planner with the proposed domain can generate plans that reduce response time and operational cost for disaster response and resilience planning.