[pt] As técnicas de aprendizado profundo têm mostrado contribuições significativas em vários campos, incluindo a análise de imagens. A grande maioria dos trabalhos em visão computacional concentra-se em propor e aplicar novos modelos e algoritmos de aprendizado de máquina. Para tarefas de aprendizado supervisionado, o desempenho dessas técnicas depende de uma grande quantidade de dados de treinamento, bem como de dados rotulados. No entanto, a rotulagem é um processo caro e demorado. Uma recente área de exploração são as reduções dos esforços na preparação de dados, deixando-os sem inconsistências, ruídos, para que os modelos atuais possam obter um maior desempenho. Esse novo campo de estudo é chamado de Data-Centric IA. Apresentamos uma nova abordagem baseada em Deep Reinforcement Learning (DRL), cujo trabalho é voltado para a preparação de um conjunto de dados em problemas de detecção de objetos, onde as anotações de caixas delimitadoras são feitas de modo autônomo e econômico. Nossa abordagem consiste na criação de uma metodologia para treinamento de um agente virtual a fim de rotular automaticamente os dados, a partir do auxílio humano como professor desse agente. Implementamos o algoritmo Deep Q-Network para criar o agente virtual e desenvolvemos uma abordagem de aconselhamento para facilitar a comunicação do humano professor com o agente virtual estudante. Para completar nossa implementação, utilizamos o método de aprendizado ativo para selecionar casos onde o agente possui uma maior incerteza, necessitando da intervenção humana no processo de anotação durante o treinamento. Nossa abordagem foi avaliada e comparada com outros métodos de aprendizado por reforço e interação humano-computador, em diversos conjuntos de dados, onde o agente virtual precisou criar novas anotações na forma de caixas delimitadoras. Os resultados mostram que o emprego da nossa metodologia impacta positivamente para obtenção de novas anotações a partir de um conjunto de dados com rótulos escassos, superando métodos existentes. Desse modo, apresentamos a contribuição no campo de Data-Centric IA, com o desenvolvimento de uma metodologia de ensino para criação de uma abordagem autônoma com aconselhamento humano para criar anotações econômicas a partir de anotações escassas. / [en] Deep learning techniques have shown significant contributions in various fields, including image analysis. The vast majority of work in computer vision focuses on proposing and applying new machine learning models and algorithms. For supervised learning tasks, the performance of these techniques depends on a large amount of training data and labeled data. However, labeling is an expensive and time-consuming process. A recent area of exploration is the reduction of efforts in data preparation, leaving it without inconsistencies and noise so that current models can obtain greater performance. This new field of study is called Data-Centric AI. We present a new approach based on Deep Reinforcement Learning (DRL), whose work is focused on preparing a dataset, in object detection problems where the bounding box annotations are done autonomously and economically. Our approach consists of creating a methodology for training a virtual agent in order to automatically label the data, using human assistance as a teacher of this agent. We implemented the Deep Q-Network algorithm to create the virtual agent and developed a counseling approach to facilitate the communication of the human teacher with the virtual agent student. We used the active learning method to select cases where the agent has more significant uncertainty, requiring human intervention in the annotation process during training to complete our implementation. Our approach was evaluated and compared with other reinforcement learning methods and human-computer interaction in different datasets, where the virtual agent had to create new annotations in the form of bounding boxes. The results show that the use of our methodology has a positive impact on obtaining new annotations from a dataset with scarce labels, surpassing existing methods. In this way, we present the contribution in the field of Data-Centric AI, with the development of a teaching methodology to create an autonomous approach with human advice to create economic annotations from scarce annotations.

Robots and virtual agents grow rapidly in behavioural sophistication and complexity. They become better learners and teachers, cooperators and communicators, workers and companions. These artefacts – whose behaviours are not always readily understood by human intuition nor comprehensibly explained in terms of mechanism – will have to interact socially. Moving beyond artificial rational systems to artificial social systems means having to engage with fundamental questions about agenthood, sociality, intelligence, and the relationship between mind and body. It also means having to revise our theories about these things in the course of continuously assessing the social sufficiency of existing artificial social agents. The present thesis presents an empirical study investigating the social influence of physical versus virtual embodiment on people's decisions in the context of a bargaining task. The results indicate that agent embodiment did not affect the social influence of the agent or the extent to which it was perceived as a social actor. However, participants' perception of the agent as a social actor did influence their decisions. This suggests that experimental results from studies comparing different robot embodiments should not be over-generalised beyond the particular task domain in which the studied interactions took place.

Providing context behind customer feedback remains a challenge for company’s who rely on approaching Customer Experience (CX) through standardized Customer Satisfaction (CS) metrics like Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), and Customer Effort Score (CES). Practical guidelines for monitoring CS throughout the customer journey are limited, creating a gap in academic research. This study addresses this gap by offering practical guidelines for CS, actionable insights, and alternative survey design strategies within the context of invoicing. Utilizing a Research-through-Design (RtD) approach guided by the Double Diamond design model, the study consists of four phases: Discover, Define, Develop, and Deliver. From a service design perspective using qualitative methods, the study acquires and analyzes both organizational and customer insights. Synthesized empirical findings emphasize the need for a more comprehensive approach that targets specific phases of the customer journey utilizing a more customer- centric approach, paving the way for alternative methods that reaches beyond just simply measuring CS. Introducing the concept of a personal companion, the study presents a dialogical approach where surveys are experienced as ongoing interactions rather mere tasks. By highlighting the importance of contextualization, alternative survey approaches, and a dialogical approach, this research aims to guide company’s in managing customer feedback strategies.