Human-robot teams for disaster response need to dynamically adapt their task allocation and coordination to the momentary context, based on adequate trust stances and taking account of the relevant values and norms (such as safety, health, and privacy). This paper presents a Work Agreement framework that supports this capability. The research question is: Which minimal set of concepts, relations, and associated formalization can be used to model Work Agreements with adequate expressiveness and flexibility? An ontology has been developed that defines core concepts with their relations (creditor, debtor, antecedent, consequent, lifespan, acceptance), encompassing the knowledge to specify, activate, monitor, and reason about work agreements. The framework was implemented and tested as part of the TRADR project. The TRADR system brought forward the desired adaptive team behavior of the concerning robot. The tests led to further refinements of the work agreements framework.

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Due to advances in technology, the world around us contains an increasingnumber of robots, virtual agents, and other intelligent systems. These systems allhave a certain degree of autonomy. For the people who interact with an intelligentsystem it is important to obtain a good understanding of its degree of autonomy:what tasks can the system perform autonomously and to what extent? In this paperwe therefore present a study on how a system’s characteristics affect people’s perceptionof its autonomy. This was investigated by asking fire-fighters to rate theautonomy of a number of search and rescue robots in different shapes and situations.In this paper, we identify the following seven aspects of perceived autonomy: timeinterval of interaction, obedience, informativeness, task complexity, task implication,physical appearance, and physical distance to human operator. The study showed thatincreased disobedience, task complexity and physical distance of a robot can increaseperceived autonomy.@en

This paper describes the design and development of an embodied conversational agent (ECA) that provides a social interface for older adults. Following a user-centred design approach, we implemented a multimodal agent consisting of a virtual character and a robot. This so-called "bi-bodied conversational agent for elderly" was iteratively refined and developed through participatory design and rapid prototyping in 3 consecutive focus groups with a total of 21 elderly users. In addition to the two bodies, a Wizard-of-Oz control panel was developed, enabling researchers to control both bodies so as to respond to the user's instructions, questions, and remarks. The research resulted in a platform that can be used for future research on elderly-robot and elderly-avatar interaction. In addition, the research resulted in insights about elderly users' preferences regarding the appearance and design of a virtual and a robotic ECA (Embodied Conversational Agent), described in results that can be reused in future experiments involving ECA for elderly users.@en

This paper presents work on the design rationale and architecture of ReMindMe. ReMindMe aims to provide agent-based support for people with Alzheimer’s disease and their social environment by playing music with a strong personal meaning to the patient so as to activate personal memory recall. ReMindMe stimulates reminiscence and self-disclosure of personal memories. Through long-term interaction with the patient, the ReMindMe agent gradually constructs a knowledge base containing information about the patient’s life stories. The agent uses this knowledge base to engage in mutual conversational self-disclosure about personal memories so as to stimulate reminiscence. Future research aims to develop and refine ReMindMe through coactive design and testing 'in the wild', i.e. at dementia care facilities. The envisioned outcome of the project is a usable and effective proof-of-concept of a conversational agent for the dementia care practice.@en

Research shows that music with a strong personal meaning can enhance the social, cognitive, and affective experiences of both people with dementia (PwD) and their social environment. We applied a human-centred design method, called situated Cognitive Engineering, to develop the conceptual design and design rationale of the Music ePartner. The design rationale specifies the general knowledge-base (ontology), context (use cases), and expected effects (claims) of the ePartner support. Three functionalities
were developed through rapid prototyping: (1) annotated play lists, (2) a music and picture album, and (3) a picture slide show. Accompanied by a close relative, five PwD participated in a formative evaluation of the prototype at their regular day care centres. All participants interacted with all three functionalities of the prototype as they would in their natural setting. The researchers observed participants’ responses to the prototype using observational scoring forms, and interviewed participants about their experiences using semi-structured interviews. Results showed that the music stimulated PwD to tell life stories related to the songs. Furthermore, music evoked positive individual and group experiences. Specific constraints, additional user needs, and interaction requirements for the Music ePartner resulted in a refinement of both the requirements baseline and the design rationale.
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As intelligent technology steadily becomes a part of modern societies, people collaborate with agents more frequently, and so agents need to be socially intelligent, i.e. personalised and context-sensitive. This paper introduces a context-sen-sitive personalisation framework for social agents that fa- cilitates the establishment of human-agent relationships by sharing past experiences through personal conversation, and sharing new experiences by engaging in joint activities together. We apply the framework in a robot application for the dementia care practice: ReJAM - Robots engaging El- derly in Joint Activities with Music.@en

An intelligent system for automated scenario-based training (SBT) needs knowledge about the training domain, events taking place in the simulated environment, the behaviour of the participating characters, and teaching strategies for effective learning. This knowledge base should be theoretically sound and should represent the information in a generic, consistent, and unambiguous manner. Currently, there is no such knowledge base. This paper investigates the declarative knowledge needed for a system to reason about training and to make intelligent teaching decisions. A frame-based approach was used to model the identified knowledge in an ontology. The ontology specifies the core concepts of SBT and their relationships, and is applicable across training domains and applications. The ontology was used to develop a critical component of SBT: The scenario generator. It was found that the ontology enabled the scenario generator to develop scenarios that fitted the learning needs and skill level of the trainee. The presented work is an important step towards automated scenario-based training systems.

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