Personalised self-explanation by robots: The role of goals versus beliefs in robot-action explanation for children and adults

Kaptein, Frank; Broekens, Joost; Hindriks, Koen V.; Neerincx, Mark A.

doi:10.1109/roman.2017.8172376

Cited by 50 publications

(32 citation statements)

References 20 publications

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“…Thellman et al [26] found that people ascribe similar levels of intentionality to robots as they do to humans. Yet, inferring a robot's beliefs and desires is not always intuitive and many researchers have argued that robots should be able to explain their behavior in order to reduce uncertainty and allow for a transparent and trusting interaction [10,20,25].…”

Section: Background 21 Related Workmentioning

confidence: 99%

Effects of a Social Robot's Self-Explanations on How Humans Understand and Evaluate Its Behavior

Stange

Kopp

2020

Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

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Social robots interacting with users in real-life environments will often show surprising or even undesirable behavior. In this paper we investigate whether a robot's ability to self-explain its behavior affects the users' perception and assessment of this behavior. We propose an explanation model based on humans' folk-psychological concepts and test different explanation strategies in specifically designed HRI scenarios with robot behaviors perceived as intentional, but differently surprising or desirable. All types of explanation strategies increased the understandability and desirability of the behaviors. While merely stating an action had similar effects as giving a reason for it (an intention or need), combining both in a causal explanation helped the robot to better justify its behavior and to increase its understandability and desirability to a larger extent. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; User studies; Scenario-based design; Empirical studies in interaction design.

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Section: Background 21 Related Workmentioning

confidence: 99%

Effects of a Social Robot's Self-Explanations on How Humans Understand and Evaluate Its Behavior

Stange

Kopp

2020

Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

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“…While in traditional user interfaces such interaction mechanisms can take the shape of interactive visualizations [20,25], in agentbased systems they must be embedded in the interactive dialogue between the user and the agent. To this end, an agent can take extra care to explain its recommendations [1,19,27,38], and offer users the ability to critique the recommendations based on a set of predefined parameters [5]. Taking this approach a step further, the specificity of the user's query and their tone of voice can be used as indicators of the user's progression in the decision-making process, allowing the agent to answer the user's query with another question rather than a suggestion when it is estimated that the user is still in the orientation stage.…”

Section: The Visionmentioning

confidence: 99%

Human-Centric Preference Modeling for Virtual Agents

Knijnenburg

Hubig

2020

Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents

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Intelligent virtual agents increasingly use machine learning to model the needs and preferences of their users in order to give personalized support. In this paper we present a vision of łhumancentric modelingž that moves this practice beyond the automated estimation of users' current preferences based on observable behaviors to a modeling process that a) considers users' hidden characteristics rather than observable behaviors, b) models possible futures rather than users' current state, and c) gives users an opportunity to interactively control this modeling process. This vision focuses on the long-term needs of individual humans and a genuine betterment of society. CCS CONCEPTS • Information systems → Decision support systems; • Computing methodologies → Intelligent agents; • Human-centered computing → Human computer interaction (HCI).

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“…In a similar way, the feedback & explanation functions were tested (i.e., function R5 and claim C1 in Table 1 ). For example, Kaptein et al ( 2017 ) tested robot's self-explanation with 19 children and 19 adults in which the robot performed actions to support type 1 diabetes mellitus management. Adults showed a higher preference for goal-based explanations than children, providing a foundation for personalizing the explanation.…”

Section: Evaluations Of the Prototypesmentioning

confidence: 99%

Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management

Neerincx

Vught²,

Henkemans³

et al. 2019

Front. Robot. AI

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Social or humanoid robots do hardly show up in "the wild," aiming at pervasive and enduring human benefits such as child health. This paper presents a socio-cognitive engineering (SCE) methodology that guides the ongoing research & development for an evolving, longer-lasting human-robot partnership in practice. The SCE methodology has been applied in a large European project to develop a robotic partner that supports the daily diabetes management processes of children, aged between 7 and 14 years (i.e., Personal Assistant for a healthy Lifestyle, PAL). Four partnership functions were identified and worked out (joint objectives, agreements, experience sharing, and feedback & explanation) together with a common knowledge-base and interaction design for child's prolonged disease self-management. In an iterative refinement process of three cycles, these functions, knowledge base and interactions were built, integrated, tested, refined, and extended so that the PAL robot could more and more act as an effective partner for diabetes management. The SCE methodology helped to integrate into the human-agent/robot system: (a) theories, models, and methods from different scientific disciplines, (b) technologies from different fields, (c) varying diabetes management practices, and (d) last but not least, the diverse individual and context-dependent needs of the patients and caregivers. The resulting robotic partner proved to support the children on the three basic needs of the Self-Determination Theory: autonomy, competence, and relatedness. This paper presents the R&D methodology and the human-robot partnership framework for prolonged "blended" care of children with a chronic disease (children could use it up to 6 months; the robot in the hospitals and diabetes camps, and its avatar at home). It represents a new type of human-agent/robot systems with an evolving collective intelligence. The underlying ontology and design rationale can be used as foundation for further developments of long-duration human-robot partnerships "in the wild."

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Personalised self-explanation by robots: The role of goals versus beliefs in robot-action explanation for children and adults

Cited by 50 publications

References 20 publications

Effects of a Social Robot's Self-Explanations on How Humans Understand and Evaluate Its Behavior

Effects of a Social Robot's Self-Explanations on How Humans Understand and Evaluate Its Behavior

Human-Centric Preference Modeling for Virtual Agents

Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management

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