Modelling and Predicting Trust for Developing Proactive Dialogue Strategies in Mixed-Initiative Interaction

Abstract

In mixed-initiative user interactions, a user and an autonomous agent collaborate for solving tasks by taking interleaving actions. However, this shift of control towards the agent requires a formation of trust for the user, otherwise the assistance possibly will be rejected and becomes obsolete. One approach for fostering a trustworthy interaction is to equip an agent with proactive dialogue capabilities. However, the development of adequate proactive dialogue strategies is complex and highly user-as well as contextdependent. Inappropriate usage of proactive conversation may even do more harm than good and corrupt the human-computer trust relationship. In order to alleviate this problem, modelling and predicting a proactive system's perceived trustworthiness during an ongoing interaction is essential. Therefore, this paper presents novel work on the development of a user model for live prediction of trust during proactive interaction, incorporating user-, system-, and context-dependent features. For predicting trust, three machinelearning algorithms -support vector machine, eXtreme Gradient Boost, gated recurrent unit network -are trained and tested on a proactive dialogue corpus. The experimental results show that among the classifiers the support vector machine showed the most well-rounded performance, while the gated recurrent unit had the best accuracy. The results prove the developed user model to be reliable for predicting trust in proactive dialogue. Based on the outcomes, the usability of the proposed method in real-life scenarios is discussed and implications for developing user-adaptive proactive dialogue strategies are described. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); HCI design and evaluation methods; User models; HCI theory, concepts and models.

We Do Not Anthropomorphize a Robot Based Only on Its Cover: Context Matters too!

“…Individuals perceive a female-faced robot as warmer and more competent than a male-faced robot, which evokes more discomfort [70]. When a robot (NAO) is implicitly presented as a man by giving it stereotypically masculine characteristics, it is judged more trustworthy and competent than if it is implicitly presented as a woman, where it is then considered more pleasant [188]. However, it is important to note that these authors did not take into account the participant's gender in their analysis.…”

“…This discrepancy can potentially be explained by the methodology employed. Some studies presented robots by video or by image [187,196], whereas in [188], the participants interacted with a physically present robot.…”