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Humans make decisions and act alongside other humans to pursue bothshort-term and long-term goals. As a result of ongoing progress in areas suchas computing science and automation, humans now also interact with non-humanagents of varying complexity as part of their day-to-day activities;substantial work is being done to integrate increasingly intelligent machineagents into human work and play. With increases in the cognitive, sensory, andmotor capacity of these agents, intelligent machinery for human assistance cannow reasonably be considered to engage in joint action with humans---i.e., twoor more agents adapting their behaviour and their understanding of each otherso as to progress in shared objectives or goals. The mechanisms, conditions,and opportunities for skillful joint action in human-machine partnerships is ofgreat interest to multiple communities. Despite this, human-machine jointaction is as yet under-explored, especially in cases where a human and anintelligent machine interact in a persistent way during the course ofreal-time, daily-life experience. In this work, we contribute a virtual realityenvironment wherein a human and an agent can adapt their predictions, theiractions, and their communication so as to pursue a simple foraging task. In acase study with a single participant, we provide an example of human-agentcoordination and decision-making involving prediction learning on the part ofthe human and the machine agent, and control learning on the part of themachine agent wherein audio communication signals are used to cue its humanpartner in service of acquiring shared reward. These comparisons suggest theutility of studying human-machine coordination in a virtual realityenvironment, and identify further research that will expand our understandingof persistent human-machine joint action.

Learned human-agent decision-making, communication and joint action in a virtual reality environment