Assessing Routing Decisions of Search and Rescue Teams in Service of an Artificial Social Intelligence Agent

Volkan Ustun, Ronit Jorvekar, Nikolos Gurney, David V. Pynadath, Yunzhe Wang

Abstract: In the context of Urban Search and Rescue (USAR) missions, efficient routing performance is of paramount importance for the success of a USAR team. Artificial Social Intelligence (ASI) agents could play a crucial role in guiding and interacting with these teams, and an analysis of the routing choices made by USAR teams can offer valuable insights into their overall performance and provide guidance for interventions by ASI agents. This study capitalizes on recent advancements in Graph Neural Networks, transformers, and attention models to harness their capabilities as neural heuristics for rapidly generating near-optimal routes in routing challenges. Specifically, we propose a real-time decision framework to scrutinize and evaluate routing decisions executed by participants during the DARPA ASIST Minecraft USAR Task. This assessment involves comparing the routing decisions made by participants and routes concurrently generated and recommended by neural heuristics employing Graph Neura