As the usage of service robots becomes more sophisticated, direct communication by means of human language is required to increase the efficiency of their performance. In natural speech interaction, however, people often omit some words and rely on background knowledge or the context, resulting in ambiguity. In order to develop smarter service robots, therefore, managing the context of interaction is essential. In this correspondence, we have investigated the mixed-initiative interaction that prompts for missing information and clarifies ambiguous statements based on hierarchically designed Bayesian networks. Simulation with the Kephera II robot and a usability test have demonstrated the usefulness of the proposed method.

The service robot supports people in their daily activities, while the interaction between humans and robots seems to be an important part of its performance. Dialogue may be beneficial to the robot to increase the flexibility and facility of the interaction. Traditional robots have merely dealt with simple queries like commands, but in conversation people often omit some words because of the background knowledge or the context of the conversation. Since environments contain various uncertainties, managing the context of a dialogue or the uncertainties should be necessary to support smarter service robots. In order to establish a natural communication between people and robots, we have been investigating the use of mixed-initiative interaction that prompts for missing concepts and clarifies for spurious concepts. Hierarchically designed Bayesian networks are presented for the mixed-initiative interaction. A simulation and a real robot are constructed for the demonstration of the proposed method, and experiments also show the usefulness.