Abstract
Successful human-machine teaming often hinges on the ability of eXplainable Artificial Intelligence (XAI) to make an agent’s reasoning transparent to human teammates. Doing so requires that the agent navigate a tradeoff between revealing its reasoning to those teammates without overwhelming them with too much information. This challenge is amplified when a person is teamed with multiple agents. This amplification is not simply linear, due to the increase from 1 to N agents’ worth of reasoning content, but also due to the interdependency among the agents’ reasoning that must be made transparent as well. In this work, we examine the challenges in conveying this interdependency to people teaming with multiple agents. We also propose alternate domain-independent strategies for a team of simulated robots to generate messages about their reasoning to be conveyed to a human teammate. We illustrate these strategies through their implementation in a search-and-rescue simulation testbed.
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Acknowledgments
This work was sponsored by the U.S. Army Research Laboratory (ARL) under contract number W911NF-14-D-0005 and the Defense Advanced Research Projects Agency (DARPA) under contract number W911NF2010011. Statements and opinions expressed do not necessarily reflect the position or the policy of the United States Government or the Defense Advanced Research Projects Agency, and no official endorsements should be inferred.
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Wang, N., Pynadath, D.V., Gurney, N. (2023). The Design of Transparency Communication for Human-Multirobot Teams. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2023. Lecture Notes in Computer Science(), vol 14051. Springer, Cham. https://doi.org/10.1007/978-3-031-35894-4_23
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