Browsing by Author "Asadipour, Ali"

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    FictionalWorlds, Real Connections: Developing Community Storytelling Social Chatbots through LLMs
    (The Eurographics Association, 2023) Sun, Yuqian; Wang, Hanyi; Chan, Pok Man; Tabibi, Morteza; Zhang, Yan; Lu, Huan; Chen, Yuheng; Lee, Chang Hee; Asadipour, Ali; Pelechano, Nuria; Liarokapis, Fotis; Rohmer, Damien; Asadipour, Ali
    We address the integration of storytelling and Large Language Models (LLMs) to develop engaging and believable Social Chatbots (SCs) in community settings. Motivated by the potential of fictional characters to enhance social interactions, we introduce Storytelling Social Chatbots (SSCs) and the concept of story engineering to transform fictional game characters into "live" social entities within player communities. Our story engineering process includes three steps: (1) Character and story creation, defining the SC's personality and worldview, (2) Presenting Live Stories to the Community, allowing the SC to recount challenges and seek suggestions, and (3) Communication with community members, enabling interaction between the SC and users. We employed the LLM GPT-3 to drive our SSC prototypes, ''David" and ''Catherine," and evaluated their performance in an online gaming community, ''DE (Alias)," on Discord. Our mixed-method analysis, based on questionnaires (N=15) and interviews (N=8) with community members, reveals that storytelling significantly enhances the engagement and believability of SCs in community settings.
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    IMET 2023: Frontmatter
    (The Eurographics Association, 2023) Pelechano, Nuria; Liarokapis, Fotis; Rohmer, Damien; Asadipour, Ali; Pelechano, Nuria; Liarokapis, Fotis; Rohmer, Damien; Asadipour, Ali
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    RESenv: A Realistic Earthquake Simulation Environment based on Unreal Engine
    (The Eurographics Association, 2023) Sun, Yitong; Wang, Hanchun; Zhang, Zhejun; Diels, Cyriel; Asadipour, Ali; Pelechano, Nuria; Liarokapis, Fotis; Rohmer, Damien; Asadipour, Ali
    Earthquakes have a significant impact on societies and economies, driving the need for effective search and rescue strategies. With the growing role of AI and robotics in these operations, high-quality synthetic visual data becomes crucial. Current simulation methods, mostly focusing on single building damages, often fail to provide realistic visuals for complex urban settings. To bridge this gap, we introduce an innovative earthquake simulation system using the Chaos Physics System in Unreal Engine. Our approach aims to offer detailed and realistic visual simulations essential for AI and robotic training in rescue missions. By integrating real seismic waveform data, we enhance the authenticity and relevance of our simulations, ensuring they closely mirror real-world earthquake scenarios. Leveraging the advanced capabilities of Unreal Engine, our system delivers not only high-quality visualisations but also real-time dynamic interactions, making the simulated environments more immersive and responsive. By providing advanced renderings, accurate physical interactions, and comprehensive geological movements, our solution outperforms traditional methods in efficiency and user experience. Our simulation environment stands out in its detail and realism, making it a valuable tool for AI tasks such as path planning and image recognition related to earthquake responses. We validate our approach through three AI-based tasks: similarity detection, path planning, and image segmentation.