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.

The purpose of the study is to elaborate the Wizard-of-Oz (WOz) technique by using it at a distance for design and elicitation of requirements and requests as part of a Participatory Design process (i.e. remotely - where the wizard and participant are located in different geographical locations). This study aims at evaluating what the salient traits in the participatory discussions are by using the WOz technique at a distance, what similarities there are in function or expression between non-verbal expressions in GUI-ii and ordinary physical interviews, and how WOz at a distance fit into the current state of the Participatory design field. In an early design phase of a project, ten experimental WOz-at-a-distance sessions and five face-to-face interviews were held. The experimental sessions’ focus was on the codesign of early drafts of the interaction design for a digital tool for the planning of crisis management exercises. The interaction design was developed with a WOz system. The sessions offered co-design between a designer and a co-designer by having joint access to a graphical user interface (GUI), and to verbally communicate with each other. These sessions had a twofold approach where the focus could shift seamlessly between design and walkthrough. The results from this study show that WOz at a distance, as used in this study, offers an approach for a seamless switch between design-phase and evaluation-phase in the form of walkthroughs. Having the GUI present as a boundary object whilst discussing the GUI and its interaction design enables a natural way of designing and evaluating the functions and interaction design of the upcoming system. The results also show that even if the participatory discussion lacks the face-to-face meeting between the interlocutors there are some similarities between those discussions and the face-to-face interviews in function and use of non-verbal expressions. Participatory discussions, when using WOz technique at a distance is a method where the participatory mindset is prominent and is a flexible approach that fits well with the overarching views on Participatory design.

Denna kandidatuppsats undersöker hur user experience kan främjas i ett adaptivt talbaserat system i en fordonskontext. Att undersöka och involvera user experiencei adaptiva talbaserade system är i synnerhet viktigt i denna kontext då den primära sysslan, att köra, bör störas så lite som möjligt. Ett adaptivt system ger möjlighet att effektivisera interaktion och ett talbaserat system låter föraren fokusera på trafiken.Genom att undersöka existerande teori och utföra empiriska undersökningar i form av användartester genom Wizard of Oz-tekniken och intervjuer har vi skapat designoch dialogexempel. Dessa förslag kan främja user experience och på så vis bidra till att den interaktion som sker är effektiv och behaglig. / This bachelor thesis aims to build an understanding how user experience can be promoted in an adaptive speech-based system, which is found in the context of an automobile. Investigating this particular context and system is of importance since the primary task is to drive safely and let the driver focus on driving rather than system. An adaptive system provides the opportunity to make the interaction more effective and a speech-based system lets the driver focus on the traffic environment. By studying relevant theory and by carrying out user tests, with the technique Wizard of Oz, we have drafted design and dialogue recommendations. These recommendations can promote user experience and make the interaction of the system more effective and pleasant. / Safe Speech for Knowledge (SSK)

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.

Stripped of the intent of its author, L. Frank Baum, the children's fairy tale The Wonderful Wizard of Oz was left to be understood only within a changing cultural construct. Historian Hayden White, arguing that the similarities between a novel and a work of history were more significant than their differences, insisted that history was preeminently a subsection of literature. According to White, historical narratives were manifestly verbal fictions, and the only acceptable grounds upon which the historian should choose his historical perspective were the moral and the aesthetic. White conflated historical consciousness with myth and blurred the boundary that had long divided history from fiction. Just as changing cultural concerns infused the Dorothy of Baum's children's literature with meaning so social, cultural, and moral imperatives came to dictate the content of historical stories particularly in the historiography of the Reconstruction era. The twenty first century conception of Reconstruction is different from the conception influential at the start of the twentieth. In assessing the scholarship of William A. Dunning, contemporary historians have adopted a new paradigm when describing the scholar's Reconstruction accounts. Modern commentators reject Dunning's authorial intention and the contextual framework needed to define it. Thus, Dunning has receded into the "realm of myth." Careful attendance to Dunning's historical context, contemporary audience, and his authorial intent, will reposition the perspective for analysis of Dunning's work. Removing Dunning from abstract analysis will allow historians to arrive at an understanding of his work, and view the importance of the real Dunning, rather than the fabricated image constructed from a partial and even fragmented reading of his work. / Taking Dunning on his own terms restores a meaningful past and brings into bas-relief the tremendous advances the U. S. of twenty first century has made in reshaping social and political patterns.Taking theReconstruction era on its own terms impels historians to move beyond Dunning and return in their research to revisit primary records and documents as they work to clear the grisly ground of Reconstruction historiography for further fruitful examination. / by Kathleen P. Barsalou. / Thesis (Ph.D.)--Florida Atlantic University, 2008. / Includes bibliography. / Electronic reproduction. Boca Raton, FL : 2008 Mode of access: World Wide Web.

This thesis presents work done during the last ten years on developing five multimodal spoken dialogue systems, and the empirical user studies that have been conducted with them. The dialogue systems have been multimodal, giving information both verbally with animated talking characters and graphically on maps and in text tables. To be able to study a wider rage of user behaviour each new system has been in a new domain and with a new set of interactional abilities. The five system presented in this thesis are: The Waxholm system where users could ask about the boat traffic in the Stockholm archipelago; the Gulan system where people could retrieve information from the Yellow pages of Stockholm; the August system which was a publicly available system where people could get information about the author Strindberg, KTH and Stockholm; the AdAptsystem that allowed users to browse apartments for sale in Stockholm and the Pixie system where users could help ananimated agent to fix things in a visionary apartment publicly available at the Telecom museum in Stockholm. Some of the dialogue systems have been used in controlled experiments in laboratory environments, while others have been placed inpublic environments where members of the general public have interacted with them. All spoken human-computer interactions have been transcribed and analyzed to increase our understanding of how people interact verbally with computers, and to obtain knowledge on how spoken dialogue systems canutilize the regularities found in these interactions. This thesis summarizes the experiences from building these five dialogue systems and presents some of the findings from the analyses of the collected dialogue corpora. / QC 20100611

情報処理学会研究報告音声言語情報処理;2002-SLP-44-23

情報処理学会研究報告. SLP, 音声言語情報処理; 2003-SLP-49-46 第5回音声言語シンポジウム

Language is the principal medium for ideas, while dialogue is the most natural and effective way for humans to interact with and access information from machines. Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact on usability and perceived quality. Many commonly used NLG systems employ rules and heuristics, which tend to generate inflexible and stylised responses without the natural variation of human language. However, the frequent repetition of identical output forms can quickly make dialogue become tedious for most real-world users. Additionally, these rules and heuristics are not scalable and hence not trivially extensible to other domains or languages. A statistical approach to language generation can learn language decisions directly from data without relying on hand-coded rules or heuristics, which brings scalability and flexibility to NLG. Statistical models also provide an opportunity to learn in-domain human colloquialisms and cross-domain model adaptations. A robust and quasi-supervised NLG model is proposed in this thesis. The model leverages a Recurrent Neural Network (RNN)-based surface realiser and a gating mechanism applied to input semantics. The model is motivated by the Long-Short Term Memory (LSTM) network. The RNN-based surface realiser and gating mechanism use a neural network to learn end-to-end language generation decisions from input dialogue act and sentence pairs; it also integrates sentence planning and surface realisation into a single optimisation problem. The single optimisation not only bypasses the costly intermediate linguistic annotations but also generates more natural and human-like responses. Furthermore, a domain adaptation study shows that the proposed model can be readily adapted and extended to new dialogue domains via a proposed recipe. Continuing the success of end-to-end learning, the second part of the thesis speculates on building an end-to-end dialogue system by framing it as a conditional generation problem. The proposed model encapsulates a belief tracker with a minimal state representation and a generator that takes the dialogue context to produce responses. These features suggest comprehension and fast learning. The proposed model is capable of understanding requests and accomplishing tasks after training on only a few hundred human-human dialogues. A complementary Wizard-of-Oz data collection method is also introduced to facilitate the collection of human-human conversations from online workers. The results demonstrate that the proposed model can talk to human judges naturally, without any difficulty, for a sample application domain. In addition, the results also suggest that the introduction of a stochastic latent variable can help the system model intrinsic variation in communicative intention much better.