AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2024
Bao, Y.; Gao, N.; Weng, D.; Chen, J.; Tian, Z.
MuseGesture: A Framework for Gesture Synthesis by Virtual Agents in VR Museum Guides Proceedings Article
In: U., Eck; M., Sra; J., Stefanucci; M., Sugimoto; M., Tatzgern; I., Williams (Ed.): Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct, pp. 337–338, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833150691-9 (ISBN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Embeddings, Gesture Generation, Intelligent Agents, Intelligent systems, Intelligent virtual agents, Language generation, Language Model, Large language model, large language models, Museum guide, Reinforcement Learning, Reinforcement learnings, Robust language understanding, Virtual agent, Virtual Agents, Virtual environments, Virtual reality museum guide, VR Museum Guides
@inproceedings{bao_musegesture_2024,
title = {MuseGesture: A Framework for Gesture Synthesis by Virtual Agents in VR Museum Guides},
author = {Y. Bao and N. Gao and D. Weng and J. Chen and Z. Tian},
editor = {Eck U. and Sra M. and Stefanucci J. and Sugimoto M. and Tatzgern M. and Williams I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214385900&doi=10.1109%2fISMAR-Adjunct64951.2024.00079&partnerID=40&md5=e71ffc28e299597557034259aab50641},
doi = {10.1109/ISMAR-Adjunct64951.2024.00079},
isbn = {979-833150691-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct},
pages = {337–338},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This paper presents an innovative framework named MuseGesture, designed to generate contextually adaptive gestures for virtual agents in Virtual Reality (VR) museums. The framework leverages the robust language understanding and generation capabilities of Large Language Models (LLMs) to parse tour narration texts and generate corresponding explanatory gestures. Through reinforcement learning and adversarial skill embeddings, the framework also generates guiding gestures tailored to the virtual museum environment, integrating both gesture types using conditional motion interpolation methods. Experimental results and user studies demonstrate that this approach effectively enables voice-command-controlled virtual guide gestures, offering a novel intelligent guiding system solution that enhances the interactive experience in VR museum environments. © 2024 IEEE.},
keywords = {Adversarial machine learning, Embeddings, Gesture Generation, Intelligent Agents, Intelligent systems, Intelligent virtual agents, Language generation, Language Model, Large language model, large language models, Museum guide, Reinforcement Learning, Reinforcement learnings, Robust language understanding, Virtual agent, Virtual Agents, Virtual environments, Virtual reality museum guide, VR Museum Guides},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents an innovative framework named MuseGesture, designed to generate contextually adaptive gestures for virtual agents in Virtual Reality (VR) museums. The framework leverages the robust language understanding and generation capabilities of Large Language Models (LLMs) to parse tour narration texts and generate corresponding explanatory gestures. Through reinforcement learning and adversarial skill embeddings, the framework also generates guiding gestures tailored to the virtual museum environment, integrating both gesture types using conditional motion interpolation methods. Experimental results and user studies demonstrate that this approach effectively enables voice-command-controlled virtual guide gestures, offering a novel intelligent guiding system solution that enhances the interactive experience in VR museum environments. © 2024 IEEE.
Bojić, L.; Ðapić, V.
The Interplay of Social and Robotics Theories in AGI Alignment: Navigating the Digital City Through Simulation-based Multi-Agent Systems Proceedings Article
In: N., Zdravkovic; University, Belgrade Tadeusa Koscuska 63 Belgrade Metropolitan; D., Domazet; University, Tadeusa Koscuska 63 Belgrade Belgrade Metropolitan; S., Lopez-Pernas; of Eastern Finland, Yliopistokatu-2 Joensuu University; M.A., Conde; de Vegazana S/N University of Leon, Leon Campus; P., Vijayakumar (Ed.): CEUR Workshop Proc., pp. 58–63, CEUR-WS, 2024, ISBN: 16130073 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial general intelligence, Artificial general intelligences, Autonomous agents, Decision making, Decision theory, Decisions makings, Human values, Intelligent Agents, Language Model, Large language model, large language models, Multi agent systems, Philosophical aspects, Robotic theory, Robotics, Robotics Theories, Simulation based approaches, Simulation platform, Simulation-Based Approach, Smart city, Social Theories, Social theory, Theoretical framework, Virtual cities, Virtual Reality
@inproceedings{bojic_interplay_2024,
title = {The Interplay of Social and Robotics Theories in AGI Alignment: Navigating the Digital City Through Simulation-based Multi-Agent Systems},
author = {L. Bojić and V. Ðapić},
editor = {Zdravkovic N. and Belgrade Tadeusa Koscuska 63 Belgrade Metropolitan University and Domazet D. and Tadeusa Koscuska 63 Belgrade Belgrade Metropolitan University and Lopez-Pernas S. and Yliopistokatu-2 Joensuu University of Eastern Finland and Conde M.A. and Leon Campus de Vegazana S/N University of Leon and Vijayakumar P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193478708&partnerID=40&md5=6a9fd04d5bbf8b876ba508bef1c09076},
isbn = {16130073 (ISSN)},
year = {2024},
date = {2024-01-01},
booktitle = {CEUR Workshop Proc.},
volume = {3676},
pages = {58–63},
publisher = {CEUR-WS},
abstract = {This study delves into the task of aligning Artificial General Intelligence (AGI) and Large Language Models (LLMs) to societal and ethical norms by using theoretical frameworks derived from social science and robotics. The expansive adoption of AGI technologies magnifies the importance of aligning AGI with human values and ethical boundaries. This paper presents an innovative simulation-based approach, engaging autonomous’digital citizens’ within a multi-agent system simulation in a virtual city environment. The virtual city serves as a platform to examine systematic interactions and decision-making, leveraging various theories, notably, Social Simulation Theory, Theory of Reasoned Action, Multi-Agent System Theory, and Situated Action Theory. The aim of establishing this digital landscape is to create a fluid platform that enables our AI agents to engage in interactions and enact independent decisions, thereby recreating life-like situations. The LLMs, embodying the personas in this digital city, operate as the leading agents demonstrating substantial levels of autonomy. Despite the promising advantages of this approach, limitations primarily lie in the unpredictability of real-world social structures. This work aims to promote a deeper understanding of AGI dynamics and contribute to its future development, prioritizing the integration of diverse societal perspectives in the process. © 2024 Copyright for this paper by its authors.},
keywords = {Artificial general intelligence, Artificial general intelligences, Autonomous agents, Decision making, Decision theory, Decisions makings, Human values, Intelligent Agents, Language Model, Large language model, large language models, Multi agent systems, Philosophical aspects, Robotic theory, Robotics, Robotics Theories, Simulation based approaches, Simulation platform, Simulation-Based Approach, Smart city, Social Theories, Social theory, Theoretical framework, Virtual cities, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
This study delves into the task of aligning Artificial General Intelligence (AGI) and Large Language Models (LLMs) to societal and ethical norms by using theoretical frameworks derived from social science and robotics. The expansive adoption of AGI technologies magnifies the importance of aligning AGI with human values and ethical boundaries. This paper presents an innovative simulation-based approach, engaging autonomous’digital citizens’ within a multi-agent system simulation in a virtual city environment. The virtual city serves as a platform to examine systematic interactions and decision-making, leveraging various theories, notably, Social Simulation Theory, Theory of Reasoned Action, Multi-Agent System Theory, and Situated Action Theory. The aim of establishing this digital landscape is to create a fluid platform that enables our AI agents to engage in interactions and enact independent decisions, thereby recreating life-like situations. The LLMs, embodying the personas in this digital city, operate as the leading agents demonstrating substantial levels of autonomy. Despite the promising advantages of this approach, limitations primarily lie in the unpredictability of real-world social structures. This work aims to promote a deeper understanding of AGI dynamics and contribute to its future development, prioritizing the integration of diverse societal perspectives in the process. © 2024 Copyright for this paper by its authors.
2023
Wang, J.; Chen, S.; Liu, Y.; Lau, R.
Intelligent Metaverse Scene Content Construction Journal Article
In: IEEE Access, vol. 11, pp. 76222–76241, 2023, ISSN: 21693536 (ISSN).
Abstract | Links | BibTeX | Tags: Bridges, Content generation, Contents constructions, Current situation, Deep learning, immersive visualization, Intelligent Agents, Metaverse, Metaverses, Solid modelling, Three dimensional computer graphics, Three dimensional displays, Three-dimensional display, Virtual Reality, Visual content, Visualization
@article{wang_intelligent_2023,
title = {Intelligent Metaverse Scene Content Construction},
author = {J. Wang and S. Chen and Y. Liu and R. Lau},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165350593&doi=10.1109%2fACCESS.2023.3297873&partnerID=40&md5=6004d639bc6313f19a1276588c6d092c},
doi = {10.1109/ACCESS.2023.3297873},
issn = {21693536 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {IEEE Access},
volume = {11},
pages = {76222–76241},
abstract = {The integration of artificial intelligence (AI) and virtual reality (VR) has revolutionized research across various scientific fields, with AI-driven VR simulations finding applications in education, healthcare, and entertainment. However, existing literature lacks a comprehensive investigation that systematically summarizes the fundamental characteristics and development trajectory of AI-generated visual content in the metaverse. This survey focuses on intelligent metaverse scene content construction, aiming to address this gap by exploring the application of AI in content generation. It investigates scene content generation, simulation biology, personalized content, and intelligent agents. Analyzing the current state and identifying common features, this survey provides a detailed description of methods for constructing intelligent metaverse scenes. The primary contribution is a comprehensive analysis of the current landscape of intelligent visual content production in the metaverse, highlighting emerging trends. The discussion on methods for constructing intelligent scene content in the metaverse suggests that in the era of intelligence, it has the potential to become the dominant approach for content creation in metaverse scenes. © 2013 IEEE.},
keywords = {Bridges, Content generation, Contents constructions, Current situation, Deep learning, immersive visualization, Intelligent Agents, Metaverse, Metaverses, Solid modelling, Three dimensional computer graphics, Three dimensional displays, Three-dimensional display, Virtual Reality, Visual content, Visualization},
pubstate = {published},
tppubtype = {article}
}
The integration of artificial intelligence (AI) and virtual reality (VR) has revolutionized research across various scientific fields, with AI-driven VR simulations finding applications in education, healthcare, and entertainment. However, existing literature lacks a comprehensive investigation that systematically summarizes the fundamental characteristics and development trajectory of AI-generated visual content in the metaverse. This survey focuses on intelligent metaverse scene content construction, aiming to address this gap by exploring the application of AI in content generation. It investigates scene content generation, simulation biology, personalized content, and intelligent agents. Analyzing the current state and identifying common features, this survey provides a detailed description of methods for constructing intelligent metaverse scenes. The primary contribution is a comprehensive analysis of the current landscape of intelligent visual content production in the metaverse, highlighting emerging trends. The discussion on methods for constructing intelligent scene content in the metaverse suggests that in the era of intelligence, it has the potential to become the dominant approach for content creation in metaverse scenes. © 2013 IEEE.
2011
Augello, Agnese; Scriminaci, Mario; Gaglio, Salvatore; Pilato, Giovanni
A Modular Framework for Versatile Conversational Agent Building Proceedings Article
In: Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011, pp. 577–582, 2011, ISBN: 978-0-7695-4373-4.
Abstract | Links | BibTeX | Tags: Conversational Agents, Intelligent Agents, Knowledge Representation, Ontologies, Semantic Computing, Semantic Spaces
@inproceedings{augelloModularFrameworkVersatile2011,
title = {A Modular Framework for Versatile Conversational Agent Building},
author = { Agnese Augello and Mario Scriminaci and Salvatore Gaglio and Giovanni Pilato},
doi = {10.1109/CISIS.2011.95},
isbn = {978-0-7695-4373-4},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011},
pages = {577--582},
abstract = {This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible. textcopyright 2011 IEEE.},
keywords = {Conversational Agents, Intelligent Agents, Knowledge Representation, Ontologies, Semantic Computing, Semantic Spaces},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible. textcopyright 2011 IEEE.
Pilato, Giovanni; Augello, Agnese; Gaglio, Salvatore
A Modular Architecture for Adaptive ChatBots Proceedings Article
In: Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011, pp. 177–180, 2011, ISBN: 978-0-7695-4492-2.
Abstract | Links | BibTeX | Tags: Chatbots, Conversational Agents, Intelligent Agents, Knowledge Representation, Semantic Computing
@inproceedings{pilatoModularArchitectureAdaptive2011,
title = {A Modular Architecture for Adaptive ChatBots},
author = { Giovanni Pilato and Agnese Augello and Salvatore Gaglio},
doi = {10.1109/ICSC.2011.68},
isbn = {978-0-7695-4492-2},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011},
pages = {177--180},
abstract = {We illustrate an architecture for a conversational agent based on a modular knowledge representation. This solution provides intelligent conversational agents with a dynamic and flexible behavior. The modularity of the architecture allows a concurrent and synergic use of different techniques, making it possible to use the most adequate methodology for the management of a specific characteristic of the domain, of the dialogue, or of the user behavior. We show the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation techniques and capable to differently manage conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, whose task is to choose, time by time, the most adequate chatbot knowledge section to activate. textcopyright 2011 IEEE.},
keywords = {Chatbots, Conversational Agents, Intelligent Agents, Knowledge Representation, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
We illustrate an architecture for a conversational agent based on a modular knowledge representation. This solution provides intelligent conversational agents with a dynamic and flexible behavior. The modularity of the architecture allows a concurrent and synergic use of different techniques, making it possible to use the most adequate methodology for the management of a specific characteristic of the domain, of the dialogue, or of the user behavior. We show the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation techniques and capable to differently manage conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, whose task is to choose, time by time, the most adequate chatbot knowledge section to activate. textcopyright 2011 IEEE.
Augello, Agnese; Scriminaci, Mario; Gaglio, Salvatore; Pilato, Giovanni
A modular framework for versatile conversational agent building Proceedings Article
In: Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011, pp. 577–582, 2011, ISBN: 978-0-7695-4373-4.
Abstract | Links | BibTeX | Tags: Conversational Agents, Intelligent Agents, Knowledge Representation, Ontologies, Semantic Computing, Semantic Spaces
@inproceedings{augello_modular_2011,
title = {A modular framework for versatile conversational agent building},
author = {Agnese Augello and Mario Scriminaci and Salvatore Gaglio and Giovanni Pilato},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052692335&doi=10.1109%2fCISIS.2011.95&partnerID=40&md5=321e5590d4e49b21dd71c453692e04d7},
doi = {10.1109/CISIS.2011.95},
isbn = {978-0-7695-4373-4},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011},
pages = {577–582},
abstract = {This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible. © 2011 IEEE.},
keywords = {Conversational Agents, Intelligent Agents, Knowledge Representation, Ontologies, Semantic Computing, Semantic Spaces},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible. © 2011 IEEE.
Pilato, Giovanni; Augello, Agnese; Gaglio, Salvatore
A modular architecture for adaptive ChatBots Proceedings Article
In: Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011, pp. 177–180, 2011, ISBN: 978-0-7695-4492-2.
Abstract | Links | BibTeX | Tags: Chatbots, Conversational Agents, Intelligent Agents, Knowledge Representation, Semantic Computing
@inproceedings{pilato_modular_2011-1,
title = {A modular architecture for adaptive ChatBots},
author = {Giovanni Pilato and Agnese Augello and Salvatore Gaglio},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-81255172186&doi=10.1109%2fICSC.2011.68&partnerID=40&md5=dbd3f0eed38f766e910e14a22cde59d9},
doi = {10.1109/ICSC.2011.68},
isbn = {978-0-7695-4492-2},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011},
pages = {177–180},
abstract = {We illustrate an architecture for a conversational agent based on a modular knowledge representation. This solution provides intelligent conversational agents with a dynamic and flexible behavior. The modularity of the architecture allows a concurrent and synergic use of different techniques, making it possible to use the most adequate methodology for the management of a specific characteristic of the domain, of the dialogue, or of the user behavior. We show the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation techniques and capable to differently manage conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, whose task is to choose, time by time, the most adequate chatbot knowledge section to activate. © 2011 IEEE.},
keywords = {Chatbots, Conversational Agents, Intelligent Agents, Knowledge Representation, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
We illustrate an architecture for a conversational agent based on a modular knowledge representation. This solution provides intelligent conversational agents with a dynamic and flexible behavior. The modularity of the architecture allows a concurrent and synergic use of different techniques, making it possible to use the most adequate methodology for the management of a specific characteristic of the domain, of the dialogue, or of the user behavior. We show the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation techniques and capable to differently manage conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, whose task is to choose, time by time, the most adequate chatbot knowledge section to activate. © 2011 IEEE.