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 expand the Abstract, Links and BibTex record for each paper.
2025
Lakehal, A.; Alti, A.; Annane, B.
CORES: Context-Aware Emotion-Driven Recommendation System-Based LLM to Improve Virtual Shopping Experiences Journal Article
In: Future Internet, vol. 17, no. 2, 2025, ISSN: 19995903 (ISSN).
Abstract | Links | BibTeX | Tags: Context, Context-Aware, Customisation, Decisions makings, E- commerces, e-commerce, Emotion, emotions, Language Model, Large language model, LLM, Recommendation, Virtual environments, Virtual Reality, Virtual shopping
@article{lakehal_cores_2025,
title = {CORES: Context-Aware Emotion-Driven Recommendation System-Based LLM to Improve Virtual Shopping Experiences},
author = {A. Lakehal and A. Alti and B. Annane},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218626299&doi=10.3390%2ffi17020094&partnerID=40&md5=a0f68e273de08b2c33d03da4cb6c19bb},
doi = {10.3390/fi17020094},
issn = {19995903 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Future Internet},
volume = {17},
number = {2},
abstract = {In today’s business landscape, artificial intelligence (AI) plays a pivotal role in shopping processes and customization. As the demand for customization grows, virtual reality (VR) emerges as an innovative solution to improve users’ perception and decision making in virtual shopping experiences (VSEs). Despite its potential, limited research has explored the integration of contextual information and emotions in VR to deliver effective product recommendations. This paper presents CORES (context-aware emotion-driven recommendation system), a novel approach designed to enrich users’ experiences and to support decision making in VR. CORES combines advanced large language models (LLMs) and embedding-based context-aware recommendation strategies to provide customized products. Therefore, emotions are collected from social platforms, and relevant contextual information is matched to enable effective recommendation. Additionally, CORES leverages transformers and retrieval-augmented generation (RAG) capabilities to explain recommended items, facilitate VR visualization, and generate insights using various prompt templates. CORES is applied to a VR shop of different items. An empirical study validates the efficiency and accuracy of this approach, achieving a significant average accuracy of 97% and an acceptable response time of 0.3267s in dynamic shopping scenarios. © 2025 by the authors.},
keywords = {Context, Context-Aware, Customisation, Decisions makings, E- commerces, e-commerce, Emotion, emotions, Language Model, Large language model, LLM, Recommendation, Virtual environments, Virtual Reality, Virtual shopping},
pubstate = {published},
tppubtype = {article}
}
In today’s business landscape, artificial intelligence (AI) plays a pivotal role in shopping processes and customization. As the demand for customization grows, virtual reality (VR) emerges as an innovative solution to improve users’ perception and decision making in virtual shopping experiences (VSEs). Despite its potential, limited research has explored the integration of contextual information and emotions in VR to deliver effective product recommendations. This paper presents CORES (context-aware emotion-driven recommendation system), a novel approach designed to enrich users’ experiences and to support decision making in VR. CORES combines advanced large language models (LLMs) and embedding-based context-aware recommendation strategies to provide customized products. Therefore, emotions are collected from social platforms, and relevant contextual information is matched to enable effective recommendation. Additionally, CORES leverages transformers and retrieval-augmented generation (RAG) capabilities to explain recommended items, facilitate VR visualization, and generate insights using various prompt templates. CORES is applied to a VR shop of different items. An empirical study validates the efficiency and accuracy of this approach, achieving a significant average accuracy of 97% and an acceptable response time of 0.3267s in dynamic shopping scenarios. © 2025 by the authors.
Xu, F.; Zhou, T.; Nguyen, T.; Bao, H.; Lin, C.; Du, J.
Integrating augmented reality and LLM for enhanced cognitive support in critical audio communications Journal Article
In: International Journal of Human Computer Studies, vol. 194, 2025, ISSN: 10715819 (ISSN).
Abstract | Links | BibTeX | Tags: Audio communications, Augmented Reality, Cognitive loads, Cognitive support, Decisions makings, Language Model, Large language model, LLM, Logic reasoning, Maintenance, Operations and maintenance, Oral communication, Situational awareness
@article{xu_integrating_2025,
title = {Integrating augmented reality and LLM for enhanced cognitive support in critical audio communications},
author = {F. Xu and T. Zhou and T. Nguyen and H. Bao and C. Lin and J. Du},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208467299&doi=10.1016%2fj.ijhcs.2024.103402&partnerID=40&md5=153d095b837ee1666a7da0f7ed03362c},
doi = {10.1016/j.ijhcs.2024.103402},
issn = {10715819 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Human Computer Studies},
volume = {194},
abstract = {Operation and Maintenance (O&M) missions are often time-sensitive and accuracy-dependent, requiring rapid and precise information processing in noisy, chaotic environments where oral communication can lead to cognitive overload and impaired decision-making. Augmented Reality (AR) and Large Language Models (LLMs) offer potential for enhancing situational awareness and lowering cognitive load by integrating digital visualizations with the physical world and improving dialogue management. However, synthesizing these technologies into a real-time system that effectively aids operators remains a challenge. This study explores the integration of AR and GPT-4, an advanced LLM, in time-sensitive O&M tasks, aiming to enhance situational awareness and manage cognitive load during oral communications. A customized AR system, incorporating the Microsoft HoloLens2 for cognitive monitoring and GPT-4 for decision making assistance, was tested in a human subject experiment with 30 participants. The 2×2 factorial experiment evaluated the effects of AR and LLM assistance on task performance and cognitive load. Results demonstrated significant improvements in task accuracy and reductions in cognitive load, highlighting the effectiveness of AR and LLM integration in supporting O&M missions. These findings emphasize the need for further research to optimize operational strategies in mission critical environments. © 2024 Elsevier Ltd},
keywords = {Audio communications, Augmented Reality, Cognitive loads, Cognitive support, Decisions makings, Language Model, Large language model, LLM, Logic reasoning, Maintenance, Operations and maintenance, Oral communication, Situational awareness},
pubstate = {published},
tppubtype = {article}
}
Operation and Maintenance (O&M) missions are often time-sensitive and accuracy-dependent, requiring rapid and precise information processing in noisy, chaotic environments where oral communication can lead to cognitive overload and impaired decision-making. Augmented Reality (AR) and Large Language Models (LLMs) offer potential for enhancing situational awareness and lowering cognitive load by integrating digital visualizations with the physical world and improving dialogue management. However, synthesizing these technologies into a real-time system that effectively aids operators remains a challenge. This study explores the integration of AR and GPT-4, an advanced LLM, in time-sensitive O&M tasks, aiming to enhance situational awareness and manage cognitive load during oral communications. A customized AR system, incorporating the Microsoft HoloLens2 for cognitive monitoring and GPT-4 for decision making assistance, was tested in a human subject experiment with 30 participants. The 2×2 factorial experiment evaluated the effects of AR and LLM assistance on task performance and cognitive load. Results demonstrated significant improvements in task accuracy and reductions in cognitive load, highlighting the effectiveness of AR and LLM integration in supporting O&M missions. These findings emphasize the need for further research to optimize operational strategies in mission critical environments. © 2024 Elsevier Ltd
Dongye, X.; Weng, D.; Jiang, H.; Tian, Z.; Bao, Y.; Chen, P.
Personalized decision-making for agents in face-to-face interaction in virtual reality Journal Article
In: Multimedia Systems, vol. 31, no. 1, 2025, ISSN: 09424962 (ISSN).
Abstract | Links | BibTeX | Tags: Decision making, Decision-making process, Decisions makings, Design frameworks, Face-to-face interaction, Feed-back based, Fine tuning, Human-agent interaction, Human–agent interaction, Integrated circuit design, Intelligent virtual agents, Language Model, Large language model, Multi agent systems, Multimodal Interaction, Virtual environments, Virtual Reality
@article{dongye_personalized_2025,
title = {Personalized decision-making for agents in face-to-face interaction in virtual reality},
author = {X. Dongye and D. Weng and H. Jiang and Z. Tian and Y. Bao and P. Chen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212947825&doi=10.1007%2fs00530-024-01591-7&partnerID=40&md5=d969cd926fdfd241399f2f96dbf42907},
doi = {10.1007/s00530-024-01591-7},
issn = {09424962 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Multimedia Systems},
volume = {31},
number = {1},
abstract = {Intelligent agents for face-to-face interaction in virtual reality are expected to make decisions and provide appropriate feedback based on the user’s multimodal interaction inputs. Designing the agent’s decision-making process poses a significant challenge owing to the limited availability of multimodal interaction decision-making datasets and the complexities associated with providing personalized interaction feedback to diverse users. To overcome these challenges, we propose a novel design framework that involves generating and labeling symbolic interaction data, pre-training a small-scale real-time decision-making network, collecting personalized interaction data within interactions, and fine-tuning the network using personalized data. We develop a prototype system to demonstrate our design framework, which utilizes interaction distances, head orientations, and hand postures as inputs in virtual reality. The agent is capable of delivering personalized feedback from different users. We evaluate the proposed design framework by demonstrating the utilization of large language models for data labeling, emphasizing reliability and robustness. Furthermore, we evaluate the incorporation of personalized data fine-tuning for decision-making networks within our design framework, highlighting its importance in improving the user interaction experience. The design principles of this framework can be further explored and applied to various domains involving virtual agents. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.},
keywords = {Decision making, Decision-making process, Decisions makings, Design frameworks, Face-to-face interaction, Feed-back based, Fine tuning, Human-agent interaction, Human–agent interaction, Integrated circuit design, Intelligent virtual agents, Language Model, Large language model, Multi agent systems, Multimodal Interaction, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Intelligent agents for face-to-face interaction in virtual reality are expected to make decisions and provide appropriate feedback based on the user’s multimodal interaction inputs. Designing the agent’s decision-making process poses a significant challenge owing to the limited availability of multimodal interaction decision-making datasets and the complexities associated with providing personalized interaction feedback to diverse users. To overcome these challenges, we propose a novel design framework that involves generating and labeling symbolic interaction data, pre-training a small-scale real-time decision-making network, collecting personalized interaction data within interactions, and fine-tuning the network using personalized data. We develop a prototype system to demonstrate our design framework, which utilizes interaction distances, head orientations, and hand postures as inputs in virtual reality. The agent is capable of delivering personalized feedback from different users. We evaluate the proposed design framework by demonstrating the utilization of large language models for data labeling, emphasizing reliability and robustness. Furthermore, we evaluate the incorporation of personalized data fine-tuning for decision-making networks within our design framework, highlighting its importance in improving the user interaction experience. The design principles of this framework can be further explored and applied to various domains involving virtual agents. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
2024
Ansari, U.; Qureshi, H. A.; Soomro, N. A.; Memon, A. R.
Augmented Reality-Driven Reservoir Management Via Generative Ai: Transforming Pore-Scale Fluid Flow Simulation Proceedings Article
In: Soc. Pet. Eng. - ADIPEC, Society of Petroleum Engineers, 2024, ISBN: 978-195902549-8 (ISBN).
Abstract | Links | BibTeX | Tags: 'current, AI techniques, Augmented Reality, Decision making, Decisions makings, Efficiency, Finance, Fluid flow simulation, Fluid-flow, Gasoline, High-accuracy, Management tasks, Petroleum refining, Petroleum reservoir evaluation, Pore scale, Real- time, User interaction
@inproceedings{ansari_augmented_2024,
title = {Augmented Reality-Driven Reservoir Management Via Generative Ai: Transforming Pore-Scale Fluid Flow Simulation},
author = {U. Ansari and H. A. Qureshi and N. A. Soomro and A. R. Memon},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215124881&doi=10.2118%2f222865-MS&partnerID=40&md5=32e8ddc777be342df8196b86a4eb7c60},
doi = {10.2118/222865-MS},
isbn = {978-195902549-8 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Soc. Pet. Eng. - ADIPEC},
publisher = {Society of Petroleum Engineers},
abstract = {The current revolution of generative artificial intelligence is transforming global dynamics which is also essential to petroleum engineers for effectively completing technical tasks.Henceforth the main aim of this study is to investigate the application of generative AI techniques for improving the efficiency of petroleum reservoir management.The outcomes of this study will help in developing and implementing generative AI algorithms tailored for reservoir management tasks, including reservoir modeling, production optimization, and decision support.In this study generative AI technique is employed to integrate with augmented reality (AR) to enhance reservoir management.The methodology involves developing a generative AI model to simulate pore-scale fluid flow, validated against experimental data.AR is utilized to visualize and interact with the simulation results in a real-time, immersive environment.The integration process includes data preprocessing, model training, and AR deployment.Performance metrics such as accuracy, computational efficiency, and user interaction quality are evaluated to assess the effectiveness of the proposed approach in transforming traditional reservoir management practices.The developed generative AI model demonstrated high accuracy in simulating pore-scale fluid flow, closely matching experimental data with a correlation coefficient of 0.95.The AR interface provided an intuitive visualization, significantly improving user comprehension and decision-making efficiency.Computational efficiency was enhanced by 40% compared to traditional methods, enabling real-time simulations and interactions.Moreover, it was observed that Users found the AR-driven approach more engaging and easier to understand, with a reported 30% increase in correct decision-making in reservoir management tasks.The integration of generative AI with AR allowed for dynamic adjustments and immediate feedback, which was particularly beneficial in complex scenarios requiring rapid analysis and response.Concludingly, the combination of generative AI and AR offers a transformative approach to reservoir management, enhancing both the accuracy of simulations and the effectiveness of user interactions.This methodology not only improves computational efficiency but also fosters better decision-making through immersive visualization.Future work will focus on refining the AI model and expanding the AR functionalities to cover a broader range of reservoir conditions and management strategies.This study introduces a novel integration of generative AI and augmented reality (AR) for reservoir management, offering a pioneering approach to pore-scale fluid flow simulation.By combining high-accuracy AI-driven simulations with real-time, immersive AR visualizations, this methodology significantly enhances user interaction and decision-making efficiency.This innovative framework transforms traditional practices, providing a more engaging, efficient, and accurate tool for managing complex reservoir systems. Copyright 2024, Society of Petroleum Engineers.},
keywords = {'current, AI techniques, Augmented Reality, Decision making, Decisions makings, Efficiency, Finance, Fluid flow simulation, Fluid-flow, Gasoline, High-accuracy, Management tasks, Petroleum refining, Petroleum reservoir evaluation, Pore scale, Real- time, User interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
The current revolution of generative artificial intelligence is transforming global dynamics which is also essential to petroleum engineers for effectively completing technical tasks.Henceforth the main aim of this study is to investigate the application of generative AI techniques for improving the efficiency of petroleum reservoir management.The outcomes of this study will help in developing and implementing generative AI algorithms tailored for reservoir management tasks, including reservoir modeling, production optimization, and decision support.In this study generative AI technique is employed to integrate with augmented reality (AR) to enhance reservoir management.The methodology involves developing a generative AI model to simulate pore-scale fluid flow, validated against experimental data.AR is utilized to visualize and interact with the simulation results in a real-time, immersive environment.The integration process includes data preprocessing, model training, and AR deployment.Performance metrics such as accuracy, computational efficiency, and user interaction quality are evaluated to assess the effectiveness of the proposed approach in transforming traditional reservoir management practices.The developed generative AI model demonstrated high accuracy in simulating pore-scale fluid flow, closely matching experimental data with a correlation coefficient of 0.95.The AR interface provided an intuitive visualization, significantly improving user comprehension and decision-making efficiency.Computational efficiency was enhanced by 40% compared to traditional methods, enabling real-time simulations and interactions.Moreover, it was observed that Users found the AR-driven approach more engaging and easier to understand, with a reported 30% increase in correct decision-making in reservoir management tasks.The integration of generative AI with AR allowed for dynamic adjustments and immediate feedback, which was particularly beneficial in complex scenarios requiring rapid analysis and response.Concludingly, the combination of generative AI and AR offers a transformative approach to reservoir management, enhancing both the accuracy of simulations and the effectiveness of user interactions.This methodology not only improves computational efficiency but also fosters better decision-making through immersive visualization.Future work will focus on refining the AI model and expanding the AR functionalities to cover a broader range of reservoir conditions and management strategies.This study introduces a novel integration of generative AI and augmented reality (AR) for reservoir management, offering a pioneering approach to pore-scale fluid flow simulation.By combining high-accuracy AI-driven simulations with real-time, immersive AR visualizations, this methodology significantly enhances user interaction and decision-making efficiency.This innovative framework transforms traditional practices, providing a more engaging, efficient, and accurate tool for managing complex reservoir systems. Copyright 2024, Society of Petroleum Engineers.
Ding, P.; Liu, J.; Sun, M.; Li, L.; Liu, H.
Enhancing Computational Processing Performance for Generative AI Large Models with Autonomous Decision-Making in Metaverse Applications Proceedings Article
In: Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom, pp. 253–258, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833151599-7 (ISBN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, AGI (Artificial General Intelligence), Artificial general intelligence, Artificial general intelligences, Autonomous decision, Autonomous Decision-Making, Data assimilation, Data integration, Decisions makings, Digital Twin Technology, Emotion Recognition, Generative adversarial networks, Generative AI large model, Generative AI Large Models, Large models, Metaverse, Metaverses, Model Acceleration, Model Compression, Multi agent systems, Multi-agent systems, Multi-modal data, Multi-Modal Data Integration, Multiagent systems (MASs), Reinforcement Learning, Reinforcement learnings, Spatio-temporal data
@inproceedings{ding_enhancing_2024,
title = {Enhancing Computational Processing Performance for Generative AI Large Models with Autonomous Decision-Making in Metaverse Applications},
author = {P. Ding and J. Liu and M. Sun and L. Li and H. Liu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211489063&doi=10.1109%2fMetaCom62920.2024.00048&partnerID=40&md5=ae085a7d90b12c9090f5bf7a274bc7ce},
doi = {10.1109/MetaCom62920.2024.00048},
isbn = {979-833151599-7 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom},
pages = {253–258},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {We explore how to enhance the computational processing performance for generative AI large models with autonomous decision-making in metaverse applications. We first introduce the relationship between AI large models and the Metaverse. We elaborate on the application scenarios of generative AI large models in Metaverse, including real-time weather simulation, embodied intelligence of agents, dynamic environment interaction, and user emotion recognition. We then propose the method of Multi-Dimensional Optimization Generation Framework (MDOGF) to improve computational processing performance. The experiment results show great improvement in computational processing performance. © 2024 IEEE.},
keywords = {Adversarial machine learning, AGI (Artificial General Intelligence), Artificial general intelligence, Artificial general intelligences, Autonomous decision, Autonomous Decision-Making, Data assimilation, Data integration, Decisions makings, Digital Twin Technology, Emotion Recognition, Generative adversarial networks, Generative AI large model, Generative AI Large Models, Large models, Metaverse, Metaverses, Model Acceleration, Model Compression, Multi agent systems, Multi-agent systems, Multi-modal data, Multi-Modal Data Integration, Multiagent systems (MASs), Reinforcement Learning, Reinforcement learnings, Spatio-temporal data},
pubstate = {published},
tppubtype = {inproceedings}
}
We explore how to enhance the computational processing performance for generative AI large models with autonomous decision-making in metaverse applications. We first introduce the relationship between AI large models and the Metaverse. We elaborate on the application scenarios of generative AI large models in Metaverse, including real-time weather simulation, embodied intelligence of agents, dynamic environment interaction, and user emotion recognition. We then propose the method of Multi-Dimensional Optimization Generation Framework (MDOGF) to improve computational processing performance. The experiment results show great improvement in computational processing performance. © 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.
Chandrashekar, N. Donekal; Lee, A.; Azab, M.; Gracanin, D.
Understanding User Behavior for Enhancing Cybersecurity Training with Immersive Gamified Platforms Journal Article
In: Information (Switzerland), vol. 15, no. 12, 2024, ISSN: 20782489 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Critical infrastructures, Cyber attacks, Cyber security, Cyber systems, Cyber-attacks, Cybersecurity, Decisions makings, Digital infrastructures, digital twin, Extended reality, Gamification, Immersive, Network Security, simulation, Technical vulnerabilities, Training, user behavior, User behaviors
@article{donekal_chandrashekar_understanding_2024,
title = {Understanding User Behavior for Enhancing Cybersecurity Training with Immersive Gamified Platforms},
author = {N. Donekal Chandrashekar and A. Lee and M. Azab and D. Gracanin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213435167&doi=10.3390%2finfo15120814&partnerID=40&md5=134c43c7238bae4923468bc6e46c860d},
doi = {10.3390/info15120814},
issn = {20782489 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Information (Switzerland)},
volume = {15},
number = {12},
abstract = {In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure. © 2024 by the authors.},
keywords = {Artificial intelligence, Critical infrastructures, Cyber attacks, Cyber security, Cyber systems, Cyber-attacks, Cybersecurity, Decisions makings, Digital infrastructures, digital twin, Extended reality, Gamification, Immersive, Network Security, simulation, Technical vulnerabilities, Training, user behavior, User behaviors},
pubstate = {published},
tppubtype = {article}
}
In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure. © 2024 by the authors.