Online Explanation Generation for Human-Robot Teaming
PROJECT ATHENA
In this work, we argue that explanations, especially ones of a complex nature, should be made in an online fashion during the execution, which helps spread out the information to be explained and thus reduce the mental workload of humans in highly demanding tasks. However, a challenge here is that the different parts of an explanation may be dependent on each other, which must be taken into account when generating online explanations. To this end, a general formulation of online explanation generation is presented with three variations satisfying different properties. The new explanation generation methods are based on a model reconciliation setting introduced in our prior work. We evaluate our methods both with human subjects in a standard planning competition (IPC) domain, using NASA Task Load Index (TLX), as well as in simulation with ten different problems across two IPC domains. Results strongly suggest that our methods not only generate explanations that are perceived as less cognitively demanding and much preferred over the baselines but also are computationally efficient.
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Please use this link to read about our paper.