Percorrer por autor "Campos, Joana"
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- The author’s journey—understanding and Improving the authoring process of theory-driven socially intelligent agentsPublication . Guimarães, Manuel; Campos, Joana; Santos, Pedro A.; Dias, João; Prada, RuiState-of-the-art agent-modelling tools support the creation of powerful Socially Intelligent Agents (SIAs) capable of engaging in social interactions with participants in various roles and environments. However, their deployment demands a labourious authoring task as it is necessary to manually define behaviour rules and create content for different interaction scenarios. While Socially Intelligent Agents (SIAs) research has centred on the user experience, we shift focus to the authors. To understand the challenges faced by authors who create these agents, we performed an innovative analysis of the authoring experience in modern agent modelling tools. One key finding is that, while SIA concepts are generally understandable, emotional-based concepts are not as easily comprehended or used by authors. We propose a hybrid solution approach that culminated in the development of Authoring-Assisted FAtiMA-Toolkit. The augmented agent modelling tool incorporates a data-driven Authoring Assistant to boost author productivity while promoting transparency and authorial control. To evaluate the impact of this framework on the authoring experience, we conducted a user study. Results showed that authors using the Authoring-Assisted FAtiMA-Toolkit were on average able to create more SIA-related content in less time. Our findings suggest that data-augmented, theory-grounded agent modelling tools can support the development of affective social agents by reducing the authoring burden without sacrificing the framework’s clarity or the authors’ control over the content.
- EEG Mode: emotional episode generation for social sharing of emotionsPublication . Antunes, Ana; Campos, Joana; Dias, João; Santos, Pedro A.; Prada, RuiSocial sharing of emotions (SSE) occurs when one communicates their feelings and reactions to a certain event in the course of a social interaction. The phenomenon is part of our social fabric and plays an important role in creating empathetic responses and establishing rapport. Intelligent social agents capable of SSE will have a mechanism to create and build long-term interaction with humans. In this paper, we present the Emotional Episode Generation (EEG) model, a fine-tuned GPT-2 model capable of generating emotional social talk regarding multiple event tuples in a human-like manner. Human evaluation results show that the model successfully translates one or more event-tuples into emotional episodes, reaching quality levels close to human performance. Furthermore, the model clearly expresses one emotion in each episode as well as humans. To train this model we used a public dataset and built upon it using event extraction techniques(1).
- MHeVA - mental health virtual assistant for high education studentsPublication . Antunes, André; Guimarães, Manuel; Santos, Pedro A.; Dias, João; Boura, Carla; Campos, JoanaCurrent Higher Education Institutions' mental health support systems lack the capabilities to cope with the growing need and demand for mental health support from students. We introduce MHeVA - Mental Health Virtual Assistant - which was designed with the goal of creating an intelligent virtual agent that could serve as a first-line diagnostic-aid tool for mental health services across universities and faculties. Students interact with the agent which attempts to establish rapport and promotes disclosure through mental health state evaluation questions. In addition to this, MHeVA has the ability to assess self-reported anxiety levels, provide health improvement tips and flag the most severe cases. In order to evaluate the agent's effectiveness, a user study was conducted that measured self-disclosure, rapport building, anxiety levels and stigma mitigation. Our findings suggest that MHeVA was able to elicit self-disclosure in higher education students and achieved high levels of acceptance and engagement. The work presented further supports the potential benefits of using IVAs to encourage self-disclosure and to be integrated into existing mental health care systems.
- Prompting for socially intelligent agents with chatGPTPublication . Antunes, Ana; Campos, Joana; Guimarães, Manuel; Dias, João; Santos, Pedro A.Socially Intelligent Agents (SIAs) have become increasingly popular in various contexts, including education and entertainment. However, creating complex social scenarios tailored to a designer's specific goals remains a significant challenge. The authoring burden can be substantial, limiting the potential of SIAs to deliver rich, engaging experiences. In this work, we propose leveraging the extensive knowledge stored within Large Language Models and use theory-driven prompting to extract social practices and identify appropriate social affordances for a scenario description. Our prompting approach aims to guide the system into considering the essential components (beliefs and desires) necessary to produce intentions, actions, and emotions(1). Results show that our approach produces large amounts of accurate and new information that can add value to the scenario. However, the process can introduce inaccuracies without human supervision.