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Navigation is critical for everyday tasks but is especially important for urban search and rescue (USAR) contexts. Aside from successful navigation, individuals must also be able to effectively communicate spatial information. This study investigates how differences in spatial ability affected overall performance in a USAR task in a simulated Minecraft environment and the effectiveness of an individual’s ability to communicate their location verbally. Randomly selected participants were asked to rescue as many victims as possible in three 10-minute missions. Results showed that sense of direction may not predict the ability to communicate spatial information, and that the skill of processing spatial information may be distinct from the ability to communicate spatial information to others. We discuss the implications of these findings for teaming contexts that involve both processes. 

Abstract Artificial social intelligence (ASI) agents have great potential to aid the success of individuals, human–human teams, and human–artificial intelligence teams. To develop helpful ASI agents, we created an urban search and rescue task environment in Minecraft to evaluate ASI agents’ ability to infer participants’ knowledge training conditions and predict participants’ next victim type to be rescued. We evaluated ASI agents’ capabilities in three ways: (a) comparison to ground truth—the actual knowledge training condition and participant actions; (b) comparison among different ASI agents; and (c) comparison to a human observer criterion, whose accuracy served as a reference point. The human observers and the ASI agents used video data and timestamped event messages from the testbed, respectively, to make inferences about the same participants and topic (knowledge training condition) and the same instances of participant actions (rescue of victims). Overall, ASI agents performed better than human observers in inferring knowledge training conditions and predicting actions. Refining the human criterion can guide the design and evaluation of ASI agents for complex task environments and team composition.