Can a robot understand a human's social behavior? Moreover, how should a robot act in response to a human's behavior? If the goals of artificial intelligence are to understand, imitate, and interact with human level intelligence then researchers must also explore the social underpinnings of this intellect. Our endeavor is buttressed by work in biology, neuroscience, social psychology and sociology. Initially developed by Kelley and Thibaut, social psychology's interdependence theory serves as a conceptual skeleton for the study of social situations, a computational process of social deliberation, and relationships (Kelley&Thibaut, 1978). We extend and expand their original work to explore the challenge of interaction with an embodied, situated robot. This dissertation investigates the use of outcome matrices as a means for computationally representing a robot's interactions. We develop algorithms that allow a robot to create these outcome matrices from perceptual information and then to use them to reason about the characteristics of their interactive partner. This work goes on to introduce algorithms that afford a means for reasoning about a robot's relationships and the trustworthiness of a robot's partners. Overall, this dissertation embodies a general, principled approach to human-robot interaction which results in a novel and scientifically meaningful approach to topics such as trust and relationships.

In a home environment, there are many tasks that a human may need to accomplish. These activities, which range from picking up a telephone to clearing rooms in the house, all have the common trend of fetching. These tasks can only be completed correctly with the consideration of many things including an understanding of what the human wants, recognition of the correct item from the environment, and manipulation and grasping of the object of interest. The focus of this work is on addressing one aspect of this problem, decomposing an image scene such that a task-specific object of interest can be identified. In this work, communication between human and robot is represented using a feedback formalism. This involves the back-and-forth transfer of textual information between the human and the robot such that the robot receives all information necessary to recognize the task-specific object of interest. We name this new communication mechanism Interactive Text Response (ITR), which we believe will provide a novel contribution to the field of Human Robot Interaction. The methodology employed involves capturing a view of the scene that contains an object of interest. Then, the robot makes inquiries based on its current understanding of the scene to disambiguate between objects in the scene. In this work, we discuss development of ITR in human-robot interaction, and understanding of variability, ease of recognition, clutter, and workload needed to develop an interactive robot system.

The purpose of this qualitative case study is to evaluate an early stage survey skill developed for the social robot Furhat, and look into how the user experience (UX) and voice user interface (VUI) of that skill can be improved. Several qualitative methods have been used: expert evaluations using heuristics for human-robot interaction (HRI), user evaluations including observations and interviews, as well as a quantitative questionnaire (RoSAS – Robot Social Attribution Scale). The empirical findings have been classified into the USUS Evaluation Framework for Human-Robot Interaction. The user evaluations were performed in two modes, one group of informants talked and interacted with Furhat with the support of a graphical user interface (GUI), and the other group without the GUI. A positive user experience was identified in both modes, showing that the informants found interacting with Furhat a fun, engaging and interesting experience. The mode with the supportive GUI could be suitable in noisy environments, and for longer surveys with many response alternatives to choose from, whereas the other mode could work better for less noisy environments and for shorter surveys. General improvements that can contribute to a better user experience in both modes were found; such as having the robot adopt a more human-like character when it comes to the dialogue and the facial expressions and movements, along with addressing a number of technical and usability issues. / Syftet med den här kvalitativa fallstudien är att utvärdera en enkätskill för den sociala roboten Furhat. Förutom utvärderingen av denna skill, som är i ett tidigt skede av utvecklingen, är syftet även att undersöka hur användarupplevelsen (UX) och röstgränssnittet (VUI) kan förbättras. Olika kvalitativa metoder har använts: expertutvärderingar med heuristik för MRI (människa-robot-interaktion), användarutvärderingar bestående av observationer och intervjuer, samt ett kvantitativt frågeformulär (RoSAS – Robot Social Attribution Scale). Resultaten från dessa har placerats in i ramverket USUS Evaluation Framework for Human- Robot Interaction. Användarutvärderingarna utfördes i två olika grupper: en grupp pratade och interagerade med Furhat med stöd av ett grafiskt användargränssnitt (GUI), den andra hade inget GUI. En positiv användarupplevelse konstaterades i båda grupperna: informanterna tyckte att det var roligt, engagerande och intressant att interagera med Furhat. Att ha ett GUI som stöd kan passa bättre för bullriga miljöer och för längre enkäter med många svarsalternativ att välja bland, medan ett GUI inte behövs för lugnare miljöer och kortare enkäter. Generella förbättringar som kan bidra till att höja användarupplevelsen hittades i båda grupperna; till exempel att roboten bör agera mer människolikt när det kommer till dialogen och ansiktsuttryck och rörelser, samt att åtgärda ett antal tekniska problem och användbarhetsproblem.

Heme is one of the most important and most studied cofactors that are essential for proper function of many proteins. Heme-containing proteins comprise of a large group of biologically important molecules that are involved in many physiological processes. The presented dissertation is focused on two groups of heme sensor proteins, namely prokaryotic heme-based gas sensors and eukaryotic heme-responsive sensors. Heme-based gas sensors play an important role in regulation of many bacterial processes and consist usually of two domains, a sensor domain and a functional domain. The dissertation thesis aims at the study of two model bacterial heme-based gas sensors, histidine kinase AfGcHK and diguanylate cyclase YddV, in order to elucidate their mechanism of interdomain signal transduction. Using X-ray crystallography and hydrogen-deuterium exchange coupled to mass spectrometry approaches, significant differences in the structure of the AfGcHK protein between the active and inactive forms were described. The signal detection by the AfGcHK sensor domain affects the structural properties of the protein, and these conformational changes then have indirect impact on the enzyme activity of the functional domain. Further, the dissertation pays more attention to the effect of a sensor domain dimerization...

As the field of Artificial Intelligence continues to evolve into a tool of societal impact, a need of breaking its initial boundaries as a computer science discipline arises to also include different humanistic fields. The work presented in this thesis revolves around the role that explainable artificial intelligence has in human-robot interaction through the study of poetry generators. To better understand the scope of the project, a poetry generators study presents the steps involved in the development process and the evaluation methods. In the algorithmic development of poetry generators, the shift from traditional disciplines to transdisciplinarity is identified. In collaboration with researchers from the Research Institutes of Sweden, state-of-the-art generators are tested to showcase the power of artificially enhanced artifacts. A development plateau is discovered and with the inclusion of Design Thinking methods potential future human-robot interaction development is identified. A physical prototype capable of verbal interaction on top of a poetry generator is created with the new feature of changing the corpora to any given audio input. Lastly, the strengths of transdisciplinarity are connected with the open-sourced community in regards to creativity and self-expression, producing an online tool to address future work improvements and introduce nonexperts to the steps required to self-build an intelligent robotic companion, thus also encouraging public technological literacy. Explainable AI is shown to help with user involvement in the process of creation, alteration and deployment of AI enhanced applications.

In a regular classroom setting, a more emotionally expressive teacher has been shown to be positively correlated with learning outcomes and task motivation among the students. Would this also be the case if the teacher were a robot? This study investigates how the facial expressions of an anthropomorphic robot affect learning outcomes and task motivation in adult learners studying a second language by setting up an experiment using the Furhat robot. The same interactions were carried out by an experiment group and a control group. The results show no increase in performance in either learning outcomes or task performance. Future studies are suggested to investigate other potential factors of why robots outperform regular computers in these settings. / I ett vanligt klassrumssammanhang har det visats att lärare som visar känslouttryck har en positiv korrelation med läranderesultat och motivation hos studenterna. Skulle detta också vara fallet om läraren var en robot? Den här studien undersöker hur ansiktsuttrycken hos en människoliknande robot påverkar läranderesultat och motivation hos vuxna studenter som lär sig ett andraspråk genom ett experiment med roboten Furhat. Samma interaktioner genomfördes av både en experimentgrupp och en kontrollgrupp. Resultaten visar ingen ökning av varken läranderesultat eller motivation. Framtida studier föreslås att undersöka andra potentiella faktorer som bidrar till att robotar utpresterar människor i dessa sammanhang.

Datorstyrda maskiner som både kan styra sina egna aktiviteter och som har ett stort rörelseomfång kommer snart att dela vår fysiska miljö vilket kommer innebära en drastisk förändring för vår nuvarande mänskliga kontext. Tidigare olyckor som skett mellan mänskliga förare och automatiserade fordon kan förklaras genom en bristande förståelse för de automatiserade fordonets beteende. Det är därför viktigt att ta reda på hur människor förstår automatiserade fordons förmågor och begränsningar. SAE International, en global yrkeskår får ingenjörer verksamma inom fordonsindustrin, har definierat ett ramverk som beskriver funktionaliteten hos automatiserade fordon i 6 olika nivåer. Den rapporterade studien undersökte med utgångspunkt i detta ramverk vilken automationsgrad deltagarna antar att en självkörande buss har genom deltagarnas upplevelse av fordonet. Inom ramarna för studien färdades deltagarna en kort sträcka på en självkörande buss och besvarade en enkät om hur de ser på bussens förmågor och begränsningar både före och efter färden. Studieresultatet visade att hälften av deltagarna överskattade bussens automationsgrad. Efter att ha färdats med bussen justerade deltagarna ner sina förväntningar på fordonets körförmåga vilket stämde bättre överens med bussens förmågor och begränsningar. Deltagarna rapporterade även att de var mer säkra i sina bedömningar efter erfarenhet av fordonet. Sammanfattningsvis tyder resultatet på att (1) människor tenderar att överskatta automatiserade fordons körförmåga, men att (2) deras uppfattning justeras i samband med att de kommer i kontakt med det automatiserade fordonet i verkligheten och att (3) de då även blir mer säkra i sina bedömningar. Detta borde tas i beaktning vid utveckling av självkörande fordon för att minska risken för olyckor i trafiken.

Machines are designed to be infallible, but what happens if they are suddenly struck by chronic mental decline such as dementia? In this research, a social robot has been transformed into a mild-stage Alzheimer’s patient. The ultimate goal is to use it as a training tool for caregivers and medical students, as well as to raise general awareness for the disease. In particular, the study aimed to identify how to simulate Alzheimer’s with a social robot and what the effects are on its conversation partners. Thanks to its properties, the back-projected robotic head Furhat was the ideal candidate to adopt the role of Max. The sources of inspiration derived from interviews and observations. A Wizard of Oz setup enabled a conversation between the character and the user, who was given the task of asking about the robot’s life. To allow for in-between subject comparisons, the set of 20 participants was a mixture of medical and non- medical students, as well as people who knew someone with dementia closely and those who never met any. The experience was evaluated through pre- and post-interviews along with user observations. The results indicate that the patient simulation was convincing, leading the users to treat the machine as a human being and develop an emotional bond to it. They remained patient in spite of the robot’s symptoms, which affirms its potential for educational use. After all, this project aims to inspire researchers to find solutions in unconventional ways.

The rapid development of social robots, designed to interact with humans, has led to increased research on user acceptance and emotions in human-robot interaction. Social acceptance is an important area to investigate if the development of social robots is to be useful. Investigating how people feel about social robots is one tool to assess acceptance toward them, and research has shown that positive emotions could invoke higher acceptance. Possible factors that have been shown to affect peoples’ attitudes regarding social robots is (1) the human-likeness and appearance of the robot and (2) the application area of the robot. Therefore, this thesis research questions address the effect of human-likeness and application areas of social robots on people's emotions. The findings indicate that in the context of companionship, people have varying emotional responses based on the appearance of the social robot. Highly human-like robots evoke more positive emotions, while low human-likeness robots elicit more negative emotions. This suggests that individuals prefer human-like social robots in intimate interactions like companionship. The results also reveal an effect of application areas, where people respond more positively to highly human-like robots used for tasks like lecturing students or companionship for older adults. Regarding less human-like social robots, people tend to respond with greater positive emotions when used within commerce. This suggests that a simpler-looking robot with low human-likeness is more suitable for commercial applications. Negative emotions expressed in the healthcare condition may reflect mistrust in robots' abilities and the sensitivity of the healthcare area. Developers and designers should consider the emotional responses that might be evoked by the task or appearance of the social robot, to ensure successful integration into society.

Portrayals of artificially intelligent robots are becoming increasingly prevalent in children’s culture. This affects how children perceive robots, which have been found to affect the way children in school understand subjects like technology and programming. Since teachers need to know what influences their pupils' understanding of these subjects, we need to know how children’s preconceptions about robots affect the way they attribute mental states to them. We still know relatively little about how children do this. Based on the above, a qualitative approach was deemed fit. This study aimed to (1) investigate the reasoning and preconceptions underlying children’s mental state attribution to robots, and (2) explore the effectiveness of moral questions as a way to do this. 16 children aged 5- and 6 years old were asked to rate the mental states of four different robots while subsequently being asked to explain their answers. Half of the children were interviewed alone and half in small groups. A thematic analysis was conducted to analyze the qualitative data. Children’s mental state attribution was found to be influenced by preconceptions about robots as a group of entities lacking mental states. Children were found to perceive two robots, Atlas, and Nao, differently in various respects. This was argued to be because the children perceived these robots through archetypal frameworks. Moral questions were found successful as a way to spark reflective reasoning about the mental state attribution in the children.