Interactional Structure Applied to the Identification and Generation of Visual Interactive Behavior: Robots that (Usually) Follow the Rules

Peer reviewe

Design Principles for Special Purpose, Embodied, Conversational Intelligence with Environmental Sensors (SPECIES) Agents

As information systems increase their ability to gather and analyze data from the natural environment and as computational power increases, the next generation of human-computer interfaces will be able to facilitate more lifelike and natural interactions with humans. This can be accomplished by using sensors to non-invasively gather information from the user, using artificial intelligence to interpret this information to perceive users’ emotional and cognitive states, and using customized interfaces and responses based on embodied-conversational-agent (avatar) technology to respond to the user. We refer to this novel and unique class of intelligent agents as Special Purpose Embodied Conversational Intelligence with Environmental Sensors (SPECIES) agents. In this paper, we build on interpersonal communication theory to specify four essential design principles of all SPECIES agents. We also share findings of initial research that demonstrates how SPECIES agents can be deployed to augment human tasks. Results of this paper organize future research efforts in collectively studying and creating more robust, influential, and intelligent SPECIES agents

A Demonstration of Continuous Interaction with Elckerlyc

We discuss behavior planning in the style of the SAIBA framework for continuous (as opposed to turn-based) interaction. Such interaction requires the real-time application of minor shape or timing modifications of running behavior and anticipation of behavior of a (human) interaction partner. We discuss how behavior (re)planning and on-the-fly parameter modification fit into the current SAIBA framework, and what type of language or architecture extensions might be necessary. Our BML realizer Elckerlyc provides flexible mechanisms for both the specification and the execution of modifications to running behavior. We show how these mechanisms are used in a virtual trainer and two turn taking scenarios

The influence of conversational agent embodiment and conversational relevance on socially desirable responding

Conversational agents (CAs) are becoming an increasingly common component in a wide range of information systems. A great deal of research to date has focused on enhancing traits that make CAs more humanlike. However, few studies have examined the influence such traits have on information disclosure. This research builds on self-disclosure, social desirability, and social presence theories to explain how CA anthropomorphism affects disclosure of personally sensitive information. Taken together, these theories suggest that as CAs become more humanlike, the social desirability of user responses will increase. In this study, we use a laboratory experiment to examine the influence of two elements of CA design—conversational relevance and embodiment—on the answers people give in response to sensitive and non-sensitive questions. We compare the responses given to various CAs to those given in a face-to-face interview and an online survey. The results show that for sensitive questions, CAs with better conversational abilities elicit more socially desirable responses from participants, with a less significant effect found for embodiment. These results suggest that for applications where eliciting honest answers to sensitive questions is important, CAs that are “better” in terms of humanlike realism may not be better for eliciting truthful responses to sensitive questions

Conversational affective social robots for ageing and dementia support

Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation and patient benefit remain immature. Affective human-robot interactions are unresolved and the deployment of robots with conversational abilities is fundamental for robustness and humanrobot engagement. In this paper, we review the state of the art within the past two decades, design trends, and current applications of conversational affective SAR for ageing and dementia support. A horizon scanning of AI voice technology for healthcare, including ubiquitous smart speakers, is further introduced to address current gaps inhibiting home use. We discuss the role of user-centred approaches in the design of voice systems, including the capacity to handle communication breakdowns for effective use by target populations. We summarise the state of development in interactions using speech and natural language processing, which forms a baseline for longitudinal health monitoring and cognitive assessment. Drawing from this foundation, we identify open challenges and propose future directions to advance conversational affective social robots for: 1) user engagement, 2) deployment in real-world settings, and 3) clinical translation

ECA gesture strategies for robust SLDSs

This paper explores the use of embodied conversational agents (ECAs) to improve interaction with spoken language dialogue systems (SLDSs). For this purpose we have identified typical interaction problems with SLDSs and associated with each of them a particular ECA gesture or behaviour. User tests were carried out dividing the test users into two groups, each facing a different interaction metaphor (one with an ECA in the interface, and the other implemented only with voice). Our results suggest user frustration is lower when an ECA is present in the interface, and the dialogue flows more smoothly, partly due to the fact that users are better able to tell when they are expected to speak and whether the system has heard and understood. The users’ overall perceptions regarding the system were also affected, and interaction seems to be more enjoyable with an ECA than without it

Evaluation of ECA Gesture strategies for robust Human-Computer Interaction

Embodied Conversational Agents (ECAs) offer us the possibility to design pleasant and efficient human-machine interaction. In this paper we present an evaluation scheme to compare dialogue-based speaker authentication and information retrieval systems with and without ECAs on the interface. We used gestures and other visual cues to improve fluency and robustness of interaction with these systems. Our tests results suggest that when an ECA is present users perceive fewer system errors, their frustration levels are lower, turn-changing goes more smoothly, the interaction experience is more enjoyable, and system capabilities are generally perceived more positively than when no ECA is present. However, the ECA seems to intensify the users' privacy concerns