Abstract-- Trust and the perception of trustworthiness play an important role in decision-making and our behaviour towards others, and this is true not only of human-human interactions but also of human-robot interactions. While significant advances have been made in recent years in the field of social robotics, there is still some way to go before we fully understand the factors that influence human trust in robots. This paper presents the results of a study into the first impressions created by a social robot's facial features, based on the hypothesis that a'babyface' engenders trust. By manipulating the back-projected face of a Furhat robot, the study confirms that eye shape and size have a significant impact on the perception of trustworthiness. The work thus contributes to an understanding of the design choices that need to be made when developing social robots so as to optimise the effectiveness of human-robot interaction. Trust is a fundamental building block for any society to function properly.

Weblogs, comprised of records detailing user activities on any website, offer valuable insights into user preferences, behavior, and interests. Numerous recommendation algorithms, employing strategies such as collaborative filtering, content-based filtering, and hybrid methods, leverage the data mined through these weblogs to provide personalized recommendations to users. Despite the abundance of information available in these weblogs, identifying and extracting pertinent information and key features from them necessitate extensive engineering endeavors. The intricate nature of the data also poses a challenge for interpretation, especially for non-experts. In this study, we introduce a sophisticated and interactive recommendation framework denoted as InteraRec, which diverges from conventional approaches that exclusively depend on weblogs for recommendation generation. InteraRec framework captures high-frequency screenshots of web pages as users navigate through a website. Leveraging state-of-the-art multimodal large language models (MLLMs), it extracts valuable insights into user preferences from these screenshots by generating a textual summary based on predefined keywords. Subsequently, an LLM-integrated optimization setup utilizes this summary to generate tailored recommendations. Through our experiments, we demonstrate the effectiveness of InteraRec in providing users with valuable and personalized offerings. Furthermore, we explore the integration of session-based recommendation systems into the InteraRec framework, aiming to enhance its overall performance. Finally, we curate a new dataset comprising of screenshots from product web pages on the Amazon website for the validation of the InteraRec framework. Detailed experiments demonstrate the efficacy of the InteraRec framework in delivering valuable and personalized recommendations tailored to individual user preferences.

As intelligent agents transition from controlled to uncontrolled environments, they face challenges that sometimes exceed their operational capabilities. In many scenarios, they rely on assistance from bystanders to overcome those challenges. Using robots that get stuck in urban settings as an example, we investigate how agents can prompt bystanders into providing assistance. We conducted four focus group sessions with 17 participants that involved bodystorming, where participants assumed the role of robots and bystander pedestrians in role-playing activities. Generating insights from both assumed robot and bystander perspectives, we were able to identify potential non-verbal help-seeking strategies (i.e., addressing bystanders, cueing intentions, and displaying emotions) and factors shaping the assistive behaviours of bystanders. Drawing on these findings, we offer design considerations for help-seeking urban robots and other agents operating in uncontrolled environments to foster casual collaboration, encompass expressiveness, align with agent social categories, and curate appropriate incentives.

This paper addresses the question of how to increase the acceptability of a robot hug and whether such a hug contributes to well-being. It combines the lead author's own research with pioneering research by Alexis E. Block and Katherine J. Kuchenbecker. First, the basics of this area are laid out with particular attention to the work of the two scientists. The authors then present HUGGIE Project I, which largely consisted of an online survey with nearly 300 participants, followed by HUGGIE Project II, which involved building a hugging robot and testing it on 136 people. At the end, the results are linked to current research by Block and Kuchenbecker, who have equipped their hugging robot with artificial intelligence to better respond to the needs of subjects.

The link between social isolation and health problems among seniors has been well-documented, and the Covid-19 pandemic has only worsened the issue. One startup that is attempting to tackle the problem with a round-the-clock virtual companion providing social support and access to care when needed is care.coach, The company offers a tablet with a virtual avatar that can be used in a couple of different ways to support seniors and others in need, like the developmentally disabled, at home. First, the user can initiate an interaction with the avatar, a puppy or kitten, by tapping its head on the tablet screen, said Victor Wang, founder and CEO of the Millbrae, California-based company, in a phone interview. Historically, this initiated a phone call to the care.coach

Current discussions of "Explainable AI" (XAI) do not much consider the role of abduction in explanatory reasoning (see Mueller, et al., 2018). It might be worthwhile to pursue this, to develop intelligent systems that allow for the observation and analysis of abductive reasoning and the assessment of abductive reasoning as a learnable skill. Abductive inference has been defined in many ways. For example, it has been defined as the achievement of insight. Most often abduction is taken as a single, punctuated act of syllogistic reasoning, like making a deductive or inductive inference from given premises. In contrast, the originator of the concept of abduction---the American scientist/philosopher Charles Sanders Peirce---regarded abduction as an exploratory activity. In this regard, Peirce's insights about reasoning align with conclusions from modern psychological research. Since abduction is often defined as "inferring the best explanation," the challenge of implementing abductive reasoning and the challenge of automating the explanation process are closely linked. We explore these linkages in this report. This analysis provides a theoretical framework for understanding what the XAI researchers are already doing, it explains why some XAI projects are succeeding (or might succeed), and it leads to design advice.

It began three and a half billion years ago in a pool of muck, when a molecule made a copy of itself and so became the ultimate ancestor of all earthly life. It began four million years ago, when brain volumes began climbing rapidly in the hominid line. In less than thirty years, it will end. Jaan Tallinn stumbled across these words in 2007, in an online essay called Staring into the Singularity. The "it" was human civilisation. Humanity would cease to exist, predicted the essay's author, with the emergence of superintelligence, or AI, that surpasses human-level intelligence in a broad array of areas. Tallinn, an Estonia-born computer programmer, has a background in physics and a propensity to approach life like one big programming problem.

In the quest to be able to keep driving in the coming years, more drivers 50 and up are eager to try the latest in car-safety tech that might help them stay behind the wheel, a new study finds. Some 76% of drivers in that age group surveyed say they would look for a car with the latest safety features, finds the online survey by insurer The Hartford. "Our findings indicate that some drivers, age 50-plus, would be more willing to drive in certain situations if they had particular technologies," said Jodi Olshevski, a gerontologist who is executive director of The Hartford Center for Mature Marketing Excellence. They study suggests "they associate advanced technologies with enhanced safety," They are looking for advanced safety features like blind-spot warning, crash mitigation, lane-departure warnings and advanced headlights. Some of new technologies pave the way toward self-driving cars, which are still a few years away.

The number of older drivers in the USA is rising rapidly. In the quest to be able to keep driving as they grow older, more Boomers are anxious to try the latest in car safety tech that might help keep them stay behind the wheel, a new study finds. Some 76% drivers age 50 say they would look for a car with the latest safety features, finds the online survey by insurer The Hartford. "Our findings indicate that some drivers, age 50-plus, would be more willing to drive in certain situations if they had particular technologies," said Jodi Olshevsky, a gerontologist who is executive director of The Hartford Center for Mature Marketing Excellence. He says it suggests "they associate advanced technologies with enhanced safety," They are looking for advanced safety features like blind-spot warning, crash mitigation, lane departure warnings and advanced headlights.