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Mutual understanding of the dialogue purpose and role of participants is vital to co-create meaning and satisfactory outcomes during human and social robot dialogues. This study explored how younger and older adults perceive roles, goals and behaviors of dialogue participants when breakdown situations occur during co-creation of meaning in a dialogue. The aim was to identify intuitive mechanisms that a social robot can use to manage breakdown situations. A set of dialogues were authored, based on activity-theoretical models of human activity and recorded in a Wizard-of-Oz setup with a Nao robot. Results of 8 (four younger and four old adults) participant interviews after observing the recorded dialogues are presented. Thematic analysis of interviews resulted into three themes of perceptions and expectations on roles and relationship, the robot’s understanding of the situation and relevant behavioral norms. Themes were translated into design implications for developing activity-centric dialogue systems to seamlessly manage breakdown situations. Future work includes implementation and further study of the proposed design implications for human robot dialogues.

Purpose: The purpose of this study is to explore how older, middle aged and younger adults perceive breakdown situations caused by lack of or inconsistent knowledge, sudden focus shifts, and conflicting intentions in dialogues between a human and a socially intelligent robot in a home environment, and how they perceive strategies to manage breakdown situations.

Methods: Scenarios embedding dialogues on health-related topics were constructed based on activity-theoretical and argumentation frameworks. Different reasons for breakdown situations and strategies to handle these were embedded. The scenarios were recorded in a Wizard-of-Oz setup, with a human actor and a Nao robot. Twenty participants between 23 and 72 years of age viewed the recordings and participated in semi-structured interviews conducted remotely. Data were analyzed qualitatively using thematic analysis.

Results: Four themes relating to breakdown situations emerged: expecting, understanding, relating, and interacting. The themes span complex human activity at different complementary levels and provide further developed understanding of breakdown situations in human–robot interaction (HRI). Older and middle-aged adults emphasized emphatic behavior and adherence to social norms, while younger adults focused on functional aspects such as gaze, response time, and length of utterances. A hierarchical taxonomy of aspects relating to breakdown situations was formed, and design implications are provided, guiding future research.

Conclusion: We conclude that a socially intelligent robot agent needs strategies to 1) construct and manage its understanding related to emotions of the human, social norms, knowledge, and motive on a higher level of meaningful human activity, 2) act accordingly, for instance, adhering to transparent social roles, and 3) resolve conflicting motives, and identify reasons to prevent and manage breakdown situations at different levels of collaborative activity. Furthermore, the novel methodology to frame the dynamics of human–robot dialogues in complex activities using Activity Theory and argumentation theory was instrumental in this work and will guide the future work on tailoring the robot's behavior.}

Grice's Cooperative Principle (CP) is one of the early theories about good communication. We propose a novel formalisation of one of the sub-components of CP, namely the maxim of quantity (MoQ). We interpret MoQ as informativeness and assume it has an intrinsic relationship to syntactic cohesion. Cohesion establishes a syntactic relationship between elements in a segment of text, and is modelled using syntactic dependency relations between words of the segment.A corpora of 1600 navigation instructions provide the setting for the two proposed metrics: syntactic cohesion for every single segment, and informativeness over all the segments of an instruction. Using a human-subject survey, the metric is evaluated qualitatively and quantitatively. Evaluation results are promising and show a close relationship between the proposed metric scores and the ratings of the participants. Overall, our work indicates that the informativeness of instructions can be captured using a simple syntactic measure.

This work presents a novel method to extract sub-structures in dialogues for the following genres: human-human task driven, human-human chit-chat, human-machine task driven, and human-machine chit-chat dialogues. The model consists of a novel semi-supervised annotation schema of syntactic features, communicative functions, dialogue policy, sequence expansion and sender information. These labels are then transformed into tuples of three, four and five segments, the tuples are used as features and modelled to learn sub-structures in above mentioned genres of dialogues with sequence-to-sequence variational autoencoders. The results analyse the latent space of generic sub-structures decomposed by PCA and ICA, showing an increase in silhouette scores for clustering of the latent space.

Background and Objective: When agents (e.g. a person and a social robot) perform a joint activity to achieve a joint goal, they require sharing a relevant group intention, which has been defined as a We-intention. In forming We-intentions, breakdown situations due to conflicts between internal and “external” intentions are unavoidable, particularly in healthcare scenarios. To study such We-intention formation and “reparation” of conflicts, this paper has a two-fold objective: introduce a general computational mechanism allowing We-intention formation and reparation in interactions between a social robot and a person; and exemplify how the formal framework can be applied to facilitate interaction between a person and a social robot for healthcare scenarios.

Method: The formal computational framework for managing We-intentions was defined in terms of Answer set programming and a Belief-Desire-Intention control loop. We exemplify the formal framework based on earlier theory-based user studies consisting of human-robot dialogue scenarios conducted in a Wizard of Oz setup, video-recorded and evaluated with 20 participants. Data was collected through semi-structured interviews, which were analyzed qualitatively using thematic analysis. N=20 participants (women n=12, men=8, age range 23-72) were part of the study. Two age groups were established for the analysis: younger participants (ages 23-40) and older participants (ages 41-72).

Results: We proved four theoretical propositions, which are well-desired characteristics of any rational social robot. In our study, most participants suggested that people were the cause of breakdown situations. Over half of the young participants perceived the social robot's avoidant behavior in the scenarios.

Conclusions: This work covered in depth the challenge of aligning the intentions of two agents (for example, in a person-robot interaction) when they try to achieve a joint goal. Our framework provides a novel formalization of the We-intentions theory from social science. The framework is supported by formal properties proving that our computational mechanism generates consistent potential plans. At the same time, the agent can handle incomplete and inconsistent intentions shared by another agent (for example, a person). Finally, our qualitative results suggested that this approach could provide an acceptable level of action/intention agreement generation and reparation from a person-centric perspective.

Joint activity between human and robot agent requires them to not only form joint intention and share a mutual understanding about it but also to determine their type of commitment. Such commitment types allows robot agent to select appropriate strategies based on what can be expected from others involved in performing the given joint activity. This work proposes an architecture embedding commitments as we-intentional modes in a belief-desire-intention (BDI) based Theory of Mind (ToM) model. Dialogue mediation gathers observations facilitating ToM to infer the joint activity and hierarchical task network (HTN) plans the execution.The work is ongoing and currently the proposed architecture is being implemented to be evaluated during human-robot interaction studies.