https://studiegids.vu.nl/en/courses/2024-2025/XB_0040After successful completion of this course, students are expected to be able to: · Articulate the core themes and challenges in the field of Social Robotics. (Knowledge and insight, Applying knowledge and insight, Judgement, ) · Understand the multi-disciplinary contributions that develop Social Robots. (Knowledge and insight, Applying knowledge and insight, Judgement, ) · Describe and evaluate main theories from various field to understand how humans interact and communicate with social robots. (Knowledge and insight, Applying knowledge and insight, Judgement, ) · Understand, describe and compare screen-based versus robot-based interaction. (Knowledge and insight, Applying knowledge and insight, Judgement, ) · Discuss and compare key findings in fundamental areas of Robot Interaction, such as typology of robots; application areas; relating to robots; psychological aspects; language understanding in robots; robots outperforming humans while being worse communicators; modeling theories for performance in robots; ethical considerations. (Knowledge and insight, Applying knowledge and insight, Judgement, ) · Develop a good understanding of up-to-date and interdisciplinary scientific research in the field and in various application areas. (Knowledge and insight, Applying knowledge and insight, Judgement, ) · Understand and apply an academic multi-disciplinary attitude, academic writing style, search for insights in complex phenomena, theoretical and empirical approaches. (Knowledge and insight, Applying knowledge and insight, Judgement, Communication, Learning skills) · Apply an analytical approach to model communication in robots. (Knowledge and insight, Applying knowledge and insight, Judgement, ) · Exercise reverse engineering, building scenarios, interaction design, concurrent algorithmic approaches. (Knowledge and insight, Applying knowledge and insight, Judgement, )What does it mean when a robot steps out of your computer in physical space and starts interacting with humans as if it were a social entity itself? This course will address various aspects that come into play when physically present robots interact with humans. This course focuses mainly on social robots, humanoids in particular, suited for communicative purposes. We will discuss both sides of the coin: perspectives from the robot-side as well as perspectives from the user-side. Differences and similarities with common computer-based AI (cf. avatars, chatbots) are discussed in comparison to human-robot interaction, exemplified by main application areas (e.g., eldercare, education). Basic models and relevant aspects of the psychology of the user and ethical aspects are also discussed. If robots start talking to humans by themselves, how can they understand each other? Lectures will therefore also address the basics of the understanding of language (e.g., Natural Language Processing (NLP)). How to translate theoretical models into effective computer models is another challenge that will not only be lectured on but also practiced. A team of lecturers with the respective expertise will lecture on the wide variety of topics in this course. Several practical tutorials/workshops accompany the lectures to gain hands-on experience regarding the complexities of human-robot interaction.Lectures on a wide variety of topics related to Robot Interaction, two times a week. Several practical tutorials/workshops will accompany the lectures as a 3rd meeting in the week. Details will be presented in the schedule/syllabus on Canvas.Examination: final examination consisting of a multiple choice test of 40 questions and 5 open essay questions; the combined grade counts for 80% of the final grade for this course. Assignments related to the workshops are also graded; the averaged grade for assignments counts for 20% of the final grade for this course. Some assignments are individually, and some are done in group. To pass the course, each assignment needs to be passed. Also, to pass the exam, the separate multiple choice and essay question sections must be passed (55%) separately to pass the exam. There is only a resit for the examination.For this course, you will read a relatively large number of journal articles, book chapters, and conference proceedings. The reading list will be announced on Canvas (in the syllabus) posted on Canvas before the start of the course. Access to journal articles/ book chapters / conference papers via Canvas, or else via the VU-library, check LibSearch, e-resources (e.g., select search in PsycInfo, EBSCOhost), else Google Scholar, or http://dx.doi.orgBachelor Artificial Intelligence (Socially Aware Computing track)A detailed syllabus will be posted on Canvas in advance (approx. a week before the start of this course).Check regular updates on Canvas.