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Orientador(es)
Resumo(s)
State-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.
Descrição
Palavras-chave
Intelligent agents Affective computing Cognitive architecture Emotions Social robot
Contexto Educativo
Citação
Editora
Association for Computing Machinery (ACM)
