  The increasingly collaborative decision-making process between humans and
agents demands a comprehensive, continuous, and unobtrusive measure of trust in
agents. The gold standard format for measuring trust, a Likert-style survey,
suffers from major limitations in dynamic human-agent interactions. We proposed
a new approach to evaluate trust in a nondirective and relational conversation.
The term nondirective refers to abstract word selections in open-ended prompts,
which can probe respondents to freely describe their attitudes. The term
relational refers to interactive conversations where respondents can clarify
their responses in followup questions. We propose a systematic process for
generating nondirective trust-based prompts by using text analysis from
previously validated trust scales. This nondirective and relational approach
provides a complementary trust measurement, which can unobtrusively elicit rich
and dynamic information on situational trust throughout a human-agent
interaction.
