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More than 18 months after it was first identified, the COVID-19 pandemic continues to restrict researchers' opportunities to conduct research in face-to-face settings. This affects studies requiring participants to be co-located, such as those that examine the play of multiplayer boardgames. We present two methods for observing the play of boardgames at a distance, supported by two case studies. We report on the value and use of both methods, and reflect on five core concepts that we observed during the studies: data collection and analysis, recruitment and participation, the temporality of play, the sociality of play and material engagement, and the researcher's role in the study. This work highlights the different considerations that online studies generate when compared to in-person play and other study methods. Future work will present an in-depth discussion of the findings of these studies and present recommendations for the adoption of these distinct methods.

The automatic generation of realistic behaviour such as tactical intercepts for Unmanned Aerial Vehicles (UAV) in air combat is a challenging problem. State-of-the-art solutions propose handcrafted algorithms and heuristics whose performance depends heavily on the initial conditions and aerodynamic properties of the UAVs involved. This paper shows how to employ domain-independent planners, embedded into professional multi-agent simulations, to implement two-level Model Predictive Control (MPC) hybrid control systems for simulated UAVs. We compare the performance of controllers using planners with others based on behaviour trees that implement real world tactics. Our results indicate that hybrid planners derive novel and effective tactics from first principles inherent to the dynamical constraints UAVs are subject to.

The automatic generation of realistic behaviour such as tactical intercepts for Unmanned Aerial Vehicles (UAV) in air combat is a challenging problem. State-of-the-art solutions propose hand–crafted algorithms and heuristics whose performance depends heavily on the initial conditions and specific aerodynamic characteristics of the UAVs involved. This demo shows the ability of domain–independent planners, embedded into simulators, to generate on–line, feed–forward, control signals that steer simulated aircraft as best suits the situation