Indiana University-Purdue University Indianapolis (IUPUI) / Just as physical appearance affects persuasion and compliance in human communication, it may also bias the processing of information from avatars, computer-animated characters, and other computer interfaces with faces. Although the most persuasive of these interfaces are often the most humanlike, they incur the greatest risk of falling into the uncanny valley, the loss of empathy associated with eerily human characters. The uncanny valley could delay the acceptance of humanlike interfaces in everyday roles. To determine the extent to which the uncanny valley affects persuasion, two experiments were conducted online with undergraduates from Indiana University. The first experiment (N = 426) presented an ethical dilemma followed by the advice of an authority figure. The authority was manipulated in three ways: depiction (recorded or animated), motion quality (smooth or jerky), and recommendation (disclose or refrain from disclosing sensitive information). Of these, only the recommendation changed opinion about the dilemma, even though the animated depiction was eerier than the human depiction. These results indicate that compliance with an authority persists even when using a realistic computer-animated double. The second experiment (N = 311) assigned one of two different dilemmas in professional ethics involving the fate of a humanlike character. In addition to the dilemma, there were three manipulations of the character’s human realism: depiction (animated human or humanoid robot), voice (recorded or synthesized), and motion quality (smooth or jerky). In one dilemma, decreasing depiction realism or increasing voice realism increased eeriness. In the other dilemma, increasing depiction realism decreased perceived competence. However, in both dilemmas realism had no significant effect on whether to punish the character. Instead, the willingness to punish was predicted in both dilemmas by narratively characterized trustworthiness. Together, the experiments demonstrate both direct and indirect effects of narratives on responses to humanlike interfaces. The effects of human realism are inconsistent across different interactions, and the effects of the uncanny valley may be suppressed through narrative characterization.

Artificial Intelligence (AI) is adopted fast and wide across consumer industries and digital marketing. This new technology has the potential to enhance online customer experience and outcomes of customer experience. However, research relating to the impact of AI is still developing and empirical evidence sparse. Taking a consumercentred approach and by adopting Social Response Theory as theoretical lens, this research addresses an overall research question pertaining to the implications of online customer experience with AI on consumer behaviour. A quantitative research strategy with positivist approach is adopted to gather a large sample (n= 489) of online consumers who have previously interacted with AI-enabled technology. The collected data is analysed statistically utilising Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM). Empirical findings show strong positive effects of anthropomorphism of AI, para-social interaction with AI, and performance expectancy of AI on all three customer experience dimensions of informativeness, entertainment and social presence. Additionally, there is strong statistical support for the positive effect of informativeness and social presence on continued purchase intentions (β= .379 and β= .315), while the effects of entertainment are less strong. The mediating effects of customer experience have been assessed, highlighting social presence as most important mediator. This research contributes to knowledge by extending previous customer experience theory and quantifying the influence of online customer experience with AI on purchase intentions and eWOM. The theoretical insights also translate into direct implications for marketing practice relating to the design, integration, and implementation of more consumer- and outcome-oriented AI applications. / Faculty of Management, Law and Social Sciences studentship

linguagem é uma marca da humanidade e da consciência, sendo a conversação (ou diálogo) uma das maneiras de comunicacão mais fundamentais que aprendemos quando crianças. Por isso uma forma de fazer um computador mais atrativo para interação com usuários é usando linguagem natural. Dos sistemas com algum grau de capacidade de linguagem desenvolvidos, o chatterbot Eliza é, provavelmente, o primeiro sistema com foco em diálogo. Com o objetivo de tornar a interação mais interessante e útil para o usuário há outras aplicações alem de chatterbots, como agentes conversacionais. Estes agentes geralmente possuem, em algum grau, propriedades como: corpo (com estados cognitivos, incluindo crenças, desejos e intenções ou objetivos); incorporação interativa no mundo real ou virtual (incluindo percepções de eventos, comunicação, habilidade de manipular o mundo e comunicar com outros agentes); e comportamento similar ao humano (incluindo habilidades afetivas). Este tipo de agente tem sido chamado de diversos nomes como agentes animados ou agentes conversacionais incorporados. Um sistema de diálogo possui seis componentes básicos. (1) O componente de reconhecimento de fala que é responsável por traduzir a fala do usuário em texto. (2) O componente de entendimento de linguagem natural que produz uma representação semântica adequada para diálogos, normalmente utilizando gramáticas e ontologias. (3) O gerenciador de tarefa que escolhe os conceitos a serem expressos ao usuário. (4) O componente de geração de linguagem natural que define como expressar estes conceitos em palavras. (5) O gerenciador de diálogo controla a estrutura do diálogo. (6) O sintetizador de voz é responsável por traduzir a resposta do agente em fala. No entanto, não há consenso sobre os recursos necessários para desenvolver agentes conversacionais e a dificuldade envolvida nisso (especialmente em línguas com poucos recursos disponíveis). Este trabalho foca na influência dos componentes de linguagem natural (entendimento e gerência de diálogo) e analisa em especial o uso de sistemas de análise sintática (parser) como parte do desenvolvimento de agentes conversacionais com habilidades de linguagem mais flexível. Este trabalho analisa quais os recursos do analisador sintático contribuem para agentes conversacionais e aborda como os desenvolver, tendo como língua alvo o português (uma língua com poucos recursos disponíveis). Para isto, analisamos as abordagens de entendimento de linguagem natural e identificamos as abordagens de análise sintática que oferecem um bom desempenho. Baseados nesta análise, desenvolvemos um protótipo para avaliar o impacto do uso de analisador sintático em um agente conversacional. / Language is a mark of humanity and conscience, with the conversation (or dialogue) as one of the most fundamental manners of communication that we learn as children. Therefore one way to make a computer more attractive for interaction with users is through the use of natural language. Among the systems with some degree of language capabilities developed, the Eliza chatterbot is probably the first with a focus on dialogue. In order to make the interaction more interesting and useful to the user there are other approaches besides chatterbots, like conversational agents. These agents generally have, to some degree, properties like: a body (with cognitive states, including beliefs, desires and intentions or objectives); an interactive incorporation in the real or virtual world (including perception of events, communication, ability to manipulate the world and communicate with others); and behavior similar to a human (including affective abilities). This type of agents has been called by several terms, including animated agents or embedded conversational agents (ECA). A dialogue system has six basic components. (1) The speech recognition component is responsible for translating the user’s speech into text. (2) The Natural Language Understanding component produces a semantic representation suitable for dialogues, usually using grammars and ontologies. (3) The Task Manager chooses the concepts to be expressed to the user. (4) The Natural Language Generation component defines how to express these concepts in words. (5) The dialog manager controls the structure of the dialogue. (6) The synthesizer is responsible for translating the agents answer into speech. However, there is no consensus about the necessary resources for developing conversational agents and the difficulties involved (especially in resource-poor languages). This work focuses on the influence of natural language components (dialogue understander and manager) and analyses, in particular the use of parsing systems as part of developing conversational agents with more flexible language capabilities. This work analyses what kind of parsing resources contributes to conversational agents and discusses how to develop them targeting Portuguese, which is a resource-poor language. To do so we analyze approaches to the understanding of natural language, and identify parsing approaches that offer good performance, based on which we develop a prototype to evaluate the impact of using a parser in a conversational agent.

Curtin Universitys Embodied Conversational Agents (ECA) combine an MPEG-4 compliant Facial Animation Engine (FAE), a Text To Emotional Speech Synthesiser (TTES), and a multi-modal Dialogue Manager (DM), that accesses a Knowledge Base (KB) and outputs Virtual Human Markup Language (VHML) text which drives the TTES and FAE. A user enters a question and an animated ECA responds with a believable and affective voice and actions. However, this response to the user is normally marked up in VHML by the KB developer to produce the required facial gestures and emotional display. A real person does not react by fixed rules but on personality, beliefs, previous experiences, and training. This thesis details the design, implementation and pilot study evaluation of an Affective Personality Model for an ECA. The thesis discusses the Email Agent system that informs a user when they have email. The system, built in Curtins ECA environment, has personality traits of Friendliness, Extraversion and Neuroticism. A small group of participants evaluated the Email Agent system to determine the effectiveness of the implemented personality system. An analysis of the qualitative and quantitative results from questionnaires is presented.

[pt] Chatbots são programas de computador que interagem com usuários utilizando linguagem natural. Desde sua origem, a tecnologia avançou significantemente e aplicações baseadas na nuvem de grandes empresas permitiram que desenvolvedores criassem chatbots inteligentes e eficientes. No entanto, não há muitas abordagens de desenvolvimento aos principais módulos de um chatbot que são flexíveis o suficiente para permitir a criação de chatbots diferentes para cada domínio, mantendo um robusto controle de diálogo na aplicação. Existem trabalhos que tentam desenvolver uma abordagem mais flexível, cada um com suas vantagens e desvantagens. Uma das vantagens mais notáveis é o uso de sistemas multiagentes para distribuir e realizar tarefas feitas por chatbots. Nesse contexto, este trabalho propõe um framework geral e flexível baseado em sistemas multiagentes para construir chatbots em um domínio escolhido pelo desenvolvedor, com controle de diálogo na aplicação. Esta solução usa uma adaptação da abordagem de estado da informação, e agentes de software, para gestão do diálogo. Para validar a arquitetura proposta, um cenário de uso com 4 chatbots de prova de conceito são analisados e discutidos. / [en] Chatbots are computer programs that interact with users using natural language. Since its inception, the technology has advanced greatly and cloud-based platforms from big companies allow developers to create intelligent and efficient chatbots. However, there are not many development approaches to the main modules of a chatbot that are flexible enough to allow the creation of different chatbots for each domain, while maintaining a robust dialogue control in the application. There have been some works that try to develop a more flexible approach, each of them with their own advantages and disadvantages. One of the most notable advantages is the use of multi-agent systems to distribute and perform the tasks performed by the chatbot. In this context, this work proposes a general and flexible architecture based on multi-agent systems for building chatbots in any domain chosen by the developer, with dialogue control in the application. This architecture uses an adaptation of the information state approach, also using software agents, to perform dialogue management. To validate the proposed architecture, an user scenario involving the implementation of 4 proof of concept chatbots is analyzed discussed.

linguagem é uma marca da humanidade e da consciência, sendo a conversação (ou diálogo) uma das maneiras de comunicacão mais fundamentais que aprendemos quando crianças. Por isso uma forma de fazer um computador mais atrativo para interação com usuários é usando linguagem natural. Dos sistemas com algum grau de capacidade de linguagem desenvolvidos, o chatterbot Eliza é, provavelmente, o primeiro sistema com foco em diálogo. Com o objetivo de tornar a interação mais interessante e útil para o usuário há outras aplicações alem de chatterbots, como agentes conversacionais. Estes agentes geralmente possuem, em algum grau, propriedades como: corpo (com estados cognitivos, incluindo crenças, desejos e intenções ou objetivos); incorporação interativa no mundo real ou virtual (incluindo percepções de eventos, comunicação, habilidade de manipular o mundo e comunicar com outros agentes); e comportamento similar ao humano (incluindo habilidades afetivas). Este tipo de agente tem sido chamado de diversos nomes como agentes animados ou agentes conversacionais incorporados. Um sistema de diálogo possui seis componentes básicos. (1) O componente de reconhecimento de fala que é responsável por traduzir a fala do usuário em texto. (2) O componente de entendimento de linguagem natural que produz uma representação semântica adequada para diálogos, normalmente utilizando gramáticas e ontologias. (3) O gerenciador de tarefa que escolhe os conceitos a serem expressos ao usuário. (4) O componente de geração de linguagem natural que define como expressar estes conceitos em palavras. (5) O gerenciador de diálogo controla a estrutura do diálogo. (6) O sintetizador de voz é responsável por traduzir a resposta do agente em fala. No entanto, não há consenso sobre os recursos necessários para desenvolver agentes conversacionais e a dificuldade envolvida nisso (especialmente em línguas com poucos recursos disponíveis). Este trabalho foca na influência dos componentes de linguagem natural (entendimento e gerência de diálogo) e analisa em especial o uso de sistemas de análise sintática (parser) como parte do desenvolvimento de agentes conversacionais com habilidades de linguagem mais flexível. Este trabalho analisa quais os recursos do analisador sintático contribuem para agentes conversacionais e aborda como os desenvolver, tendo como língua alvo o português (uma língua com poucos recursos disponíveis). Para isto, analisamos as abordagens de entendimento de linguagem natural e identificamos as abordagens de análise sintática que oferecem um bom desempenho. Baseados nesta análise, desenvolvemos um protótipo para avaliar o impacto do uso de analisador sintático em um agente conversacional. / Language is a mark of humanity and conscience, with the conversation (or dialogue) as one of the most fundamental manners of communication that we learn as children. Therefore one way to make a computer more attractive for interaction with users is through the use of natural language. Among the systems with some degree of language capabilities developed, the Eliza chatterbot is probably the first with a focus on dialogue. In order to make the interaction more interesting and useful to the user there are other approaches besides chatterbots, like conversational agents. These agents generally have, to some degree, properties like: a body (with cognitive states, including beliefs, desires and intentions or objectives); an interactive incorporation in the real or virtual world (including perception of events, communication, ability to manipulate the world and communicate with others); and behavior similar to a human (including affective abilities). This type of agents has been called by several terms, including animated agents or embedded conversational agents (ECA). A dialogue system has six basic components. (1) The speech recognition component is responsible for translating the user’s speech into text. (2) The Natural Language Understanding component produces a semantic representation suitable for dialogues, usually using grammars and ontologies. (3) The Task Manager chooses the concepts to be expressed to the user. (4) The Natural Language Generation component defines how to express these concepts in words. (5) The dialog manager controls the structure of the dialogue. (6) The synthesizer is responsible for translating the agents answer into speech. However, there is no consensus about the necessary resources for developing conversational agents and the difficulties involved (especially in resource-poor languages). This work focuses on the influence of natural language components (dialogue understander and manager) and analyses, in particular the use of parsing systems as part of developing conversational agents with more flexible language capabilities. This work analyses what kind of parsing resources contributes to conversational agents and discusses how to develop them targeting Portuguese, which is a resource-poor language. To do so we analyze approaches to the understanding of natural language, and identify parsing approaches that offer good performance, based on which we develop a prototype to evaluate the impact of using a parser in a conversational agent.

Les Agents Conversationnels Animés (ACA) sont des personnages virtuels interactifs et expressifs, dont l'aspect est très souvent « humain », exploitant différentes modalités telles que la face, le langage, les gestes, le regard ou encore la prosodie de la voix. Le but est qu'ils s'expriment en langage naturel et puissent dialoguer avec des interlocuteurs humains. Pour développer un ACA, il faut d'abord comprendre que des aspects tels que personnalité, les émotions et leur apparence sont extrêmement importants. Le travail qui est présenté dans cette thèse a pour objectif d'augmenter l'acceptabilité et la crédibilité des agents au moyen de la personnalité, considérée comme une notion centrale à l'interaction ACA-humain. On propose un modèle qui dote l'ACA de facettes de personnalité et de buts de communication « cachés » et qui module ainsi ses actions conversationnelles. Ce travail présente également une application de jeu de type “puzzle”, intégrant un ACA doté de facettes de personnalité et de buts « cachés », qui a servi de support à plusieurs expérimentations et à l'évaluation du modèle proposé. / Embodied Conversational Agents (ECAs) are intelligent software entities with an embodiment used to communicate with users, using natural language. Their purpose is to exhibit the same properties as humans in face-to-face conversation, including the ability to produce and respond to verbal and nonverbal communication. Researchers in the field of ECAs try to create agents that can be more natural, believable and easy to use. Designing an ECA requires understanding that manner, personality, emotion, and appearance are very important issues to be considered. In this thesis, we are interested in increasing believability of ECAs by placing personality at the heart of the human-agent verbal interaction. We propose a model relating personality facets and hidden communication goals that can influence ECA behaviors. Moreover, we apply our model in agents that interact in a puzzle game application. We develop five distinct personality oriented agents using an expressive communication language and a plan-based BDI approach for modeling and managing dialogue according to our proposed model. In summary, we present and test an innovative approach to model mental aspects of ECAs trying to increase their believability and to enhance human-agent affective communication. With this research, we improve the understanding on how ECAs with expressive and affective characteristics can establish and maintain long-term human-agent relationships.

linguagem é uma marca da humanidade e da consciência, sendo a conversação (ou diálogo) uma das maneiras de comunicacão mais fundamentais que aprendemos quando crianças. Por isso uma forma de fazer um computador mais atrativo para interação com usuários é usando linguagem natural. Dos sistemas com algum grau de capacidade de linguagem desenvolvidos, o chatterbot Eliza é, provavelmente, o primeiro sistema com foco em diálogo. Com o objetivo de tornar a interação mais interessante e útil para o usuário há outras aplicações alem de chatterbots, como agentes conversacionais. Estes agentes geralmente possuem, em algum grau, propriedades como: corpo (com estados cognitivos, incluindo crenças, desejos e intenções ou objetivos); incorporação interativa no mundo real ou virtual (incluindo percepções de eventos, comunicação, habilidade de manipular o mundo e comunicar com outros agentes); e comportamento similar ao humano (incluindo habilidades afetivas). Este tipo de agente tem sido chamado de diversos nomes como agentes animados ou agentes conversacionais incorporados. Um sistema de diálogo possui seis componentes básicos. (1) O componente de reconhecimento de fala que é responsável por traduzir a fala do usuário em texto. (2) O componente de entendimento de linguagem natural que produz uma representação semântica adequada para diálogos, normalmente utilizando gramáticas e ontologias. (3) O gerenciador de tarefa que escolhe os conceitos a serem expressos ao usuário. (4) O componente de geração de linguagem natural que define como expressar estes conceitos em palavras. (5) O gerenciador de diálogo controla a estrutura do diálogo. (6) O sintetizador de voz é responsável por traduzir a resposta do agente em fala. No entanto, não há consenso sobre os recursos necessários para desenvolver agentes conversacionais e a dificuldade envolvida nisso (especialmente em línguas com poucos recursos disponíveis). Este trabalho foca na influência dos componentes de linguagem natural (entendimento e gerência de diálogo) e analisa em especial o uso de sistemas de análise sintática (parser) como parte do desenvolvimento de agentes conversacionais com habilidades de linguagem mais flexível. Este trabalho analisa quais os recursos do analisador sintático contribuem para agentes conversacionais e aborda como os desenvolver, tendo como língua alvo o português (uma língua com poucos recursos disponíveis). Para isto, analisamos as abordagens de entendimento de linguagem natural e identificamos as abordagens de análise sintática que oferecem um bom desempenho. Baseados nesta análise, desenvolvemos um protótipo para avaliar o impacto do uso de analisador sintático em um agente conversacional. / Language is a mark of humanity and conscience, with the conversation (or dialogue) as one of the most fundamental manners of communication that we learn as children. Therefore one way to make a computer more attractive for interaction with users is through the use of natural language. Among the systems with some degree of language capabilities developed, the Eliza chatterbot is probably the first with a focus on dialogue. In order to make the interaction more interesting and useful to the user there are other approaches besides chatterbots, like conversational agents. These agents generally have, to some degree, properties like: a body (with cognitive states, including beliefs, desires and intentions or objectives); an interactive incorporation in the real or virtual world (including perception of events, communication, ability to manipulate the world and communicate with others); and behavior similar to a human (including affective abilities). This type of agents has been called by several terms, including animated agents or embedded conversational agents (ECA). A dialogue system has six basic components. (1) The speech recognition component is responsible for translating the user’s speech into text. (2) The Natural Language Understanding component produces a semantic representation suitable for dialogues, usually using grammars and ontologies. (3) The Task Manager chooses the concepts to be expressed to the user. (4) The Natural Language Generation component defines how to express these concepts in words. (5) The dialog manager controls the structure of the dialogue. (6) The synthesizer is responsible for translating the agents answer into speech. However, there is no consensus about the necessary resources for developing conversational agents and the difficulties involved (especially in resource-poor languages). This work focuses on the influence of natural language components (dialogue understander and manager) and analyses, in particular the use of parsing systems as part of developing conversational agents with more flexible language capabilities. This work analyses what kind of parsing resources contributes to conversational agents and discusses how to develop them targeting Portuguese, which is a resource-poor language. To do so we analyze approaches to the understanding of natural language, and identify parsing approaches that offer good performance, based on which we develop a prototype to evaluate the impact of using a parser in a conversational agent.

La réalité virtuelle joue un rôle majeur dans le développement de nouvelles technologies de l’éducation, et permet de développer des environnements virtuels pour l’apprentissage, dans lesquels, des agents virtuels intelligents jouent le rôle de tuteur. Ces agents sont censés aider les utilisateurs humains à apprendre et appliquer des procédures ayant des objectifs d’apprentissage prédéfini dans différents domaines. Nous travaillons sur la construction d’un système temps-réel capable d’entamer une interaction naturelle avec un utilisateur dans un Environnement d’Apprentissage Virtuel (EAV). Afin d’implémenter ce modèle, nous proposons d’utiliser MASCARET (Multi-Agent System for Collaborative, Adaptive & Realistic Environments for Training) comme modèle d’Environnement Virtuel Intelligent (EVI) afin de représenter la base de connaissances des agents, et de modéliser la sémantique de l’environnement virtuel et des activités des utilisateurs. Afin de formaliser l’intention des agents, nous implémentons un module cognitif dans MASCARET inspiré par l’architecture BDI (Belief-Desire-Intention) qui nous permet de générer des intentions de haut-niveau pour les agents. Dans notre modèle, ces agents sont représentés par des Agents Conversationnels Animés (ACA), qui sont basés sur la plateforme SAIBA (Situation, Agent, Intention, Behavior, Animation). Les agents conversationnels de l’environnement ont des intentions communicatives qui sont transmises à l’utilisateur via des canaux de communication naturels, notamment les actes communicatifs et les comportements verbaux et non-verbaux. Afin d’évaluer notre modèle, nous l’implémentons dans un scénario pédagogique concret pour l’apprentissage des procédures d’analyse de sang dans un laboratoire biomédical. Nous utilisons cette application afin de réaliser une expérimentation et une étude pour valider les propositions de notre modèle. L’hypothèse de notre étude est de supposer que la présence d’un ACA dans un Environnement Virtuel (EV) améliore la performance du processus d’apprentissage (ou qu’au moins, ça ne le dégrade pas) dans le contexte de l’apprentissage d’une procédure spécifique. La performance de l’utilisateur est représentée par le temps requis pour l’exécution de la procédure, le nombre d’erreurs commises et le nombre de demande d’assistance. Nous analysons les résultats de cette évaluation, ce qui confirme partiellement l’hypothèse de l’expérience et affirme que la présence de l’ACA dans l’EV ne dégrade pas la performance de l’apprenant dans le contexte de l’apprentissage d’une procédure. / Virtual Reality plays a major role in developing new educational methodologies, and allows to develop virtual environments for learning where intelligent virtual agents play the role of tutors. These agents are expected to help human users to learn and apply domain-specific procedures with predefined learning outcomes. We work on building a real-time system able to sustain natural interaction with the user in a Virtual Learning Environment (VLE). To implement this model, we propose to use the Multi-Agent System for Collaborative, Adaptive & Realistic Environments for Training (MASCARET) as an Intelligent Virtual Environment (IVE) model that provides the knowledge base to the agents and model the semantic of the virtual environment and user’s activities. To formalize the intention of the agents, we implement a cognitive module within MASCARET inspired by BDI (Belief-Desire-Intention) architecture that permits us to generate high-level intentions for the agents. Furthermore, we integrate Embodied Conversational Agents (ECA), which are based on the SAIBA (Situation, Agent, Intention, Behavior, Animation) framework. The embodied agents of the environment have communicative intentions that are transmitted to the user through natural communication channels, namely the verbal and non-verbal communicative acts and behaviors of the ECAs. To evaluate our model, we implement it in a concrete pedagogical scenario for learning blood analysis procedures in a biomedical laboratory. We use this application to settle an experiment to validate the propositions of our model. The hypothesis of this experiment is to assume that the presence of anECA in a Virtual Environment (VE) enhances the learning performance (or at least does not degrade it) in the context of a learning procedure. The performance is represented by the time of execution, the number of committed errors and the number of requests for assistance. We analyze the results of this evaluation, which partially confirms the hypothesis of the experiment and assure that having an ECA in the VLE does not degrade the performance of the learner in the context of a learning procedure.

The aim of this research project is to identify design principles for the development of CAs. In the context of this thesis, the research questions are: “According to which design principles are Conversational Agents developed?” and “How can these design principles be meaningfully categorized and described?”. For the aggregation of the design principles, the first step was a systematic literature search according to Vom Brocke et al. (2009). The systematic literature review was followed by a qualitative literature analysis according to Kuckartz (2018). The result of this work is the identification of 15 meta-requirements that could be categorised by means of three main categories and a further seven subcategories. This was followed by the declaration of seven design principles based on the subcategories and their meta-requirements.