Humans and machines harmoniously collaborating and benefiting from each other is a long lasting dream for researchers in robotics and artificial intelligence. An important feature of efficient and rewarding cooperation is the ability to assume possible problematic situations and act in advance to prevent negative outcomes. This concept of assistance is known under the term proactivity. In this article, we investigate the development and implementation of proactive dialogues for fostering a trustworthy human-computer relationship and providing adequate and timely assistance. Here, we make several contributions. A formalisation of proactive dialogue in conversational assistants is provided. The formalisation forms a framework for integrating proactive dialogue in conversational applications. Additionally, we present a study showing the relations between proactive dialogue actions and several aspects of the perceived trustworthiness of a system as well as effects on the user experience. The results of the experiments provide significant contributions to the line of proactive dialogue research. Particularly, we provide insights on the effects of proactive dialogue on the human-computer trust relationship and dependencies between proactive dialogue and user specific and situational characteristics.

The next step for intelligent dialog agents is to escape their role as silent bystanders and become proactive. Well-defined proactive behavior may improve human-machine cooperation, as the agent takes a more active role during interaction and takes off responsibility from the user. However, proactivity is a double-edged sword because poorly executed pre-emptive actions may have a devastating effect on the task outcome and the relationship with the user. For designing adequate proactive dialog strategies, we propose a novel approach including both social and task-relevant features in the dialog. Here, the primary goal is to optimize proactive behavior so that it is task-oriented - this implies high task success and efficiency - while also being socially effective by fostering user trust. Including both aspects in the reward function for training a proactive dialog agent using reinforcement learning showed the benefit of our approach for more successful human-machine cooperation.

We present the interactive assistant Robert that provides situation-adaptive support in the realisation of do-it-yourself (DIY) home improvement projects. Robert assists its users by providing comprehensive step-by-step instructions for completing th