Tracking reliability and helpfulness in agent interactions

A critical aspect of open systems such as the Internet is the interactions amongst the component agents of the system. Often this interaction is organised around social principles, in that one agent may request the help of another, and in turn may make a commitment to assist another when requested. In this paper we investigate two measures of the social responsibility of an agent known as reliability and helpfulness. Intuitively, reliability measures how good an agent is at keeping its commitments, and helpfulness measures how willing an agent is to make a commitment, when requested for help. We discuss these notions in the context of FIPA protocols. It is important to note that these measures are dependent only on the messages exchanged between the agents, and do not make any assumptions about the internal organisation of the agents. This means that these measures are both applicable to any variety of software agent, and externally verifiable, i.e. able to be calculated by anyone with access to the messages exchanged

Trust in Digital Humans

With technology advances, the interaction between organisations and consumers is evolving gradually from ‘human-to-human’ to ‘human-to-machine’, due, in part, to improvements in Artificial Intelligence (AI). One such technology, the AI-enabled digital human is unique in its combining of technology and humanness and is being adopted by firms to support customer services and other business processes. However, a number of questions arise with this new way of interacting, among which is whether people will trust a digital human in the same way that they trust people. To address this question, this study draws on technology trust theory, and examines the roles of social presence, anthropomorphism, and privacy to understand trust and people’s readiness to engage with digital humans. The results aim to benefit organisations wanting to implement AI-enabled digital-humans in the workplace

Evaluating embodied conversational agents in multimodal interfaces

Based on cross-disciplinary approaches to Embodied Conversational Agents, evaluation methods for such human-computer interfaces are structured and presented. An introductory systematisation of evaluation topics from a conversational perspective is followed by an explanation of social-psychological phenomena studied in interaction with Embodied Conversational Agents, and how these can be used for evaluation purposes. Major evaluation concepts and appropriate assessment instruments – established and new ones – are presented, including questionnaires, annotations and log-files. An exemplary evaluation and guidelines provide hands-on information on planning and preparing such endeavours

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Towards Gamified Conversational Agents for Self-Regulated Learning in Digital Education

Formal education like higher education oftentimes emphasized on strict non-digital setting. This approach can lead to issues during stressful times (e.g., Covid crisis) or when learners’ needs in general are not considered. Moreover, these times highlighted how important self-regulated learning is and how much this capability is lacking in our educational system. To address these issues, we follow an Action Design Research approach and develop a gamified conversational agent (CA) that considers the learners’ needs. We present our CA and conduct a first small-scale evaluation following a mixed-method approach. First results show that students universally liked a CA for self-regulated digital learning and many enjoyed the gamified experience which helped students to be motivated to learn. As next steps we will develop the next iteration of our CA and conduct a long-term field test at a university

The Caring Personal Agent

Self (1999) argues that the essence of having a computer-based learning system that "cares" about its learners is that the system model its learners so as to be able to adapt to their needs. In this paper we discuss the notion of personal agents who care for their "owners" by representing the owners' interests in the learning system. We contextualise this discussion by showing how such personal agents are used in I-Help, a system that promotes caring and sharing by encouraging learners to help one another. In I-Help, personal agents themselves care for their learners by helping them to discover useful information and/or to find "ready, willing, and able" peer learners who can aid them in overcoming problems

Trustworthy artificial intelligence

Artificial intelligence (AI) brings forth many opportunities to contribute to the wellbeing of individuals and the advancement of economies and societies, but also a variety of novel ethical, legal, social, and technological challenges. Trustworthy AI (TAI) bases on the idea that trust builds the foundation of societies, economies, and sustainable development, and that individuals, organizations, and societies will therefore only ever be able to realize the full potential of AI, if trust can be established in its development, deployment, and use. With this article we aim to introduce the concept of TAI and its five foundational principles (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. We further draw on these five principles to develop a data-driven research framework for TAI and demonstrate its utility by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI

Evaluating the impact of physical activity apps and wearables: interdisciplinary review

Background: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines