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Much like human-human interactions, human-machine interactions require an appropriate level of trust. If the human’s trust on the machine is ill-calibrated, the interaction’s result will be suboptimal. If a machine wishes to improve trust, it must fi rst know the state of the human’s trust in it. To this end, partially observable Markov decision processes (POMDPs) have been used to estimate trust. Given observations resulting from trust (e.g., compliance or defi ance), POMDPs can estimate the probability of high or low trust in a machine given its previous action.

Applying Clustering to Optimize Markovian Models in Human-Machine Interactions