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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. 

The goal of the Space Challenge project is to identify the challenges faced by teams in space operations and then represent those challenges in a distributed human-machine teaming scenario that resembles typical space operations and to measure the coordination dynamics across the entire system. Currently, several challenges have been identified through semi-structured interviews with nine subject matter experts (SMEs) who were astronauts or those who have experienced or have been involved with interplanetary space exploration. We conducted a thematic analysis on the interviews through an iterative process. Challenges were categorized into four categories, including, communication, training, distributed teaming, and complexity. Based on the findings, challenges and key teamwork characteristics of space operations were integrated into the initial scenario development. In addition to the scenario, we plan to use dynamical system methods to analyze team activity in real time.