Although robotics is well established as a research field, there has been relatively little work on human-robot collaboration. This type of collaboration is going to become an increasingly important issue as robots work ever more closely with humans. Clearly, there is a growing need for research on human-robot collaboration and communication between humans and robotic systems. Research into human-human communication can be used as a starting point in developing a robust human-robot collaboration system. Previous research into collaborative efforts with humans has shown that grounding, situational awareness, a common frame of reference and spatial referencing are vital in effective communication. Therefore, these items comprise a list of required attributes of an effective human-robot collaborative system. Augmented Reality (AR) is a technology for overlaying three-dimensional virtual graphics onto the user's view of the real world. It also allows for real time interaction with these virtual graphics, enabling a user to reach into the augmented world and manipulate it directly. The internal state of a robot and its intended actions can be displayed through the virtual imagery in the AR environment. Therefore, AR can bridge the divide between human and robotic systems and enable effective human-robot collaboration. This thesis describes the work involved in developing the Augmented Reality Human-Robot Collaboration (AR-HRC) System. It first garners design criteria for the system from a review of communication and collaboration in human-human interaction, the current state of Human-Robot Interaction (HRI) and related work in AR. A review of research in multimodal interfaces is then provided highlighting the benefits of using such an interface design. Therefore, an AR multimodal interface was developed to determine if this type of design improved performance over a single modality design. Indeed, the multimodal interface was found to improve performance, thereby providing the impetus to use a multimodal design approach for the AR-HRC system. The architectural design of the system is then presented. A user study conducted to determine what kind of interaction people would use when collaborating with a mobile robot is discussed and then the integration of a mobile robot is described. Finally, an evaluation of the AR-HRC system is presented.

Thesis (M.A.)--Miami University, Dept. of History, 2005. / Title from first page of PDF document. Document formatted into pages; contains [7], 53 p. : ill. Includes bibliographical references (p. 49-53).

Det webbaserade Wizard of Oz-verktyget Ozlab är ett system som möjliggör skapande ochutförande av tester med interaktiva prototyper. Syftet med denna uppsats är att undersöka ochutvärdera verktyget för att hitta brister och förändringsförslag. Denna uppsats utgör en kvalitativ studie och datainsamling har skett genom observation och intervju med Ozlab-användare. Utifrån analys av data som samlades in under observation och intervju togs flertalet förändringsförslag fram. För att kontrollera att förändringsförslagen som togs fram accepterades av andra Ozlab-användare skapades en enkät som några få utvalda användare med kunskap om Ozlab-metodiken fick svara på.  Resultatet från studien visar att Ozlab är ett webbverktyg som är accepterat av användarna men det behöver förbättras på några punkter. De punkter som är tydligast är att Ozlab behöver bättre stöd för mobila enheter, kortare svarstider vid test, dokumentation och hjälpavsnitt men även avhjälpa mindre funktionsmässiga problem.

Human behavior varies widely as does the design of spoken dialog systems (SDS). The search for predictors to match a user’s preference and efficiency for a specific dialog interface type in an SDS was the focus of this research. By using personality as described by the Five-Factor Method (FFM) and the Wizard of Oz technique for delivering three system initiatives of the SDS, participants interacted with each of the SDS initiatives in scheduling an airline flight. The three system initiatives were constructed as strict system, which did not allow the user control of the interaction; mixed system, which allowed the user some control of the interaction but with a system override; and user system, which allowed the user control of the interaction. In order to eliminate gender bias in using the FFM as the instrument, participants were matched in gender and age. Participants were 18 years old to 70 years old, passed a hearing test, had no disability that prohibited the use of the SDS, and were native English speakers. Participants completed an adult consent form, a 50-question personality assessment as described by the FFM, and the interaction with the SDS. Participants also completed a system preference indication form at the end of the interaction. Observations for efficiency were recorded on paper by the researcher. Although the findings did not show a definitive predictor for a SDS due to the small population sample, by using a multinomial regression approach to the statistical analysis, odds ratios of the data helped draw conclusions that support certain personality factors as important roles in a user’s preference and efficiency in choosing and using a SDS. This gives an area for future research. Also, the presumption that preference and efficiency always match was not supported by the results from two of the three systems. An additional area for future research was discovered in the gender data. Although not an initial part of the research, the data shows promise in predicting preference and efficiency for certain SDS. Future research is indicated.

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Observed influences of system response delay in spoken human-machine dialogues are rather ambiguous and mainly focus on perceived system quality. Studies that systematically inspect effects on cognitive performance are still lacking, and effects of individual characteristics are also often neglected. Building on benefits of cognitive training for decelerating cognitive decline, this Wizard-of-Oz study addresses both issues by testing 62 elderly participants in a dialogue-based memory training with a virtual agent. Participants acquired the method of loci with fading instructional guidance and applied it afterward to memorizing and recalling lists of German nouns. System response delays were randomly assigned, and training performance was included as potential mediator. Participants’ age, gender, and subscales of affinity for technology (enthusiasm, competence, positive and negative perception of technology) were inspected as potential moderators. The results indicated positive effects on recall performance with higher training performance, female gender, and less negative perception of technology. Additionally, memory retention and facets of affinity for technology moderated increasing system response delays. Participants also provided higher ratings in perceived system quality with higher enthusiasm for technology but reported increasing frustration with a more positive perception of technology. Potential explanations and implications for the design of spoken dialogue systems are discussed.

Natural Language (NL) mechanics are seemingly underutilized within modern game development and may be capable of inducing unexpected levels of agency within its users. This study focuses specifically on NL Input (NLI) and examines its capability of inducing an experience of agency, control, and freedom through an interactive fiction with a database searching context. To get a more nuanced result, a version of the artefact but with an NL Understanding (NLU) system will also be tested to create a baseline. Due to the limited time and resources, the NLU version will be employing the Wizard of Oz (WOZ) method. In total, five NLI tests and four NLU tests were performed and interview results indicated full experience of control and mixed experience of freedom and agency in both versions. Possible causes include the participants‘ genre preference and the limited content in the artefact.

Helping drivers improve their driving skills and become safer drivers is a problematic topic. Most drivers have a lacking self-assessment ability and consider themselves above average driving skills. This is believed to be related to the lack of continuous feedback after getting the driver’s license. This has led to initiatives to find alternative ways of coaching drivers toward better self-assessment and thereby toward safer driving. Chatbots and conversational interfaces has received increasing attention over the years and could be technologies that can solve these challenges. However, a major challenge to chatbots is that they are mostly implemented in a “one-size-fits-all” approach, and while personalization of the chatbot could solve that challenge, it ishard to achieve. In this study, personalized chatbot conversations that aim to coach drivers are examined. The aim is to create a guide that can help designers and practitioners with design decisions that needs to be considered when creating coaching chatbot conversations. The study was performed as a Wizard of Oz study, where attributes for personalization as well as coaching considerations were tested with users in two iterations to iteratively develop the guide. The findings of the study include the guide itself with its guidelines (see appendix 4), as well as insights on considerations required chatbot personalization and coaching. Regarding personalization, chatbot personality and level of control were identified as two attributes that were fit for adaptation. These can lead to social benefits as well as more tailored services to the users. For coaching, the use of follow-ups, feedback and the chatbot’s attitude are identified as necessary considerations when designing coaching chatbot conversations.

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.