Developing Fire Evacuation Simulation Through Emotion-based BDI Methodology

Paschal, Celine Haren; Shiang, Cheah Wai; Wai, Sim Keng; Khairuddin, Muhammad Asyraf bin

doi:10.30630/joiv.6.1.854

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Simulations that exploit Unity as the engine and visualization tool and the BDI model as a reference for implementing individual agents have been developed in a few domains, including large urban areas [59], fire evacuation [60], first aid emergency [61], gas and oil industry [62], and bushfires in Australia [63]. Those works are driven by an application to simulate and lack re-usability in other contexts.…”

Section: Related Work Virtual Reality and Multiagent Systems (Mas)mentioning

confidence: 99%

VEsNA, a Framework for Virtual Environments via Natural Language Agents and Its Application to Factory Automation

Gatti

Mascardi

2023

Robotics

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Automating a factory where robots are involved is neither trivial nor cheap. Engineering the factory automation process in such a way that return of interest is maximized and risk for workers and equipment is minimized is hence, of paramount importance. Simulation can be a game changer in this scenario but requires advanced programming skills that domain experts and industrial designers might not have. In this paper, we present the preliminary design and implementation of a general-purpose framework for creating and exploiting Virtual Environments via Natural language Agents (VEsNA). VEsNA takes advantage of agent-based technologies and natural language processing to enhance the design of virtual environments. The natural language input provided to VEsNA is understood by a chatbot and passed to an intelligent cognitive agent that implements the logic behind displacing objects in the virtual environment. In the complete VEsNA vision, for which this paper provides the building blocks, the intelligent agent will be able to reason on this displacement and on its compliance with legal and normative constraints. It will also be able to implement what-if analysis and case-based reasoning. Objects populating the virtual environment will include active objects and will populate a dynamic simulation whose outcomes will be interpreted by the cognitive agent; further autonomous agents, representing workers in the factory, will be added to make the virtual environment even more realistic; explanations and suggestions will be passed back to the user by the chatbot.

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Section: Related Work Virtual Reality and Multiagent Systems (Mas)mentioning

confidence: 99%

VEsNA, a Framework for Virtual Environments via Natural Language Agents and Its Application to Factory Automation

Gatti

Mascardi

2023

Robotics

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“…Emotion-based BDI methodology is created based on the Belief, Desire, and Intention architecture and the OCEAN personality behavior [1] to represent decision-making during a fire evacuation simulation while considering human emotion. The methodology in [1] is designed to represent human decision-making processes in graphical representations, which can be simply translated for the implementation phase, such as to simulate various case studies. The methodology focuses on modeling a more realistic simulation closer to real human behavior where human emotion is used as one of the factors affecting decision-making.…”

Section: Introductionmentioning

confidence: 99%

Bush Fire Simulation through Emotion-based BDI Methodology

Haren Paschal,

Bin Khairuddin,

Wai Shiang

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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This paper introduces an emotion-based BDI (Belief, desire, intention) methodology to model decision-making during fire evacuation simulations while considering human emotions. The methodology is designed to represent human decision-making processes in graphical representations, which can be simply translated for the implementation phase to simulate various case studies. The methodology utilizes the Belief, Desire, and Intention architecture and the OCEAN Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) personality behavior to represent decision-making processes graphically, making it easy to translate into a simulation. The methodology aims to create a more realistic simulation closer to real human behavior by incorporating emotions that affect decision-making. In this paper, we validate the emotion-based BDI methodology by replicating the bushfire Australia case study and benchmarking with the previous work on BDI fire evacuation. From the comparison, we found that both results share almost similar patterns. The results show "dead while still unaware" (0% vs. 0%), "dead while deciding what to do" (69% vs. 48%), "dead while defending" (6% vs. 8%), and "dead while preparing to defend" (6% vs 28%), "dead while preparing to escape" (4% vs 0%) and "dead while escaping" (15% vs 20%). The results show that in our Simulation, there is a death related to preparing to escape (4% vs 0%). However, the other causes of death have an almost similar percentage of death causes. Hence, based on the comparison, supporting and validating our emotion-oriented simulation model is considered adequate. Therefore, this emotion-based BDI methodology can systematically reproduce human cognition and emotion.

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Towards VEsNA, a Framework for Managing Virtual Environments via Natural Language Agents

Gatti

Mascardi

2022

Electron. Proc. Theor. Comput. Sci.

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Automating a factory where robots are involved is neither trivial nor cheap. Engineering the factory automation process in such a way that return of interest is maximized and risk for workers and equipment is minimized, is hence of paramount importance. Simulation can be a game changer in this scenario but requires advanced programming skills that domain experts and industrial designers might not have.In this paper we present the preliminary design and implementation of a general-purpose framework for creating and exploiting Virtual Environments via Natural language Agents (VEsNA). VEsNA takes advantage of agent-based technologies and natural language processing to enhance the design of virtual environments. The natural language input provided to VEsNA is understood by a chatbot and passed to a cognitive intelligent agent that implements the logic behind displacing objects in the virtual environment. In the VEsNA vision, the intelligent agent will be able to reason on this displacement and on its compliance to legal and normative constraints. It will also be able to implement what-if analysis and case-based reasoning. Objects populating the virtual environment will include active objects and will populate a dynamic simulation whose outcomes will be interpreted by the cognitive agent; explanations and suggestions will be passed back to the user by the chatbot.

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Developing Fire Evacuation Simulation Through Emotion-based BDI Methodology

Cited by 3 publications

References 18 publications

VEsNA, a Framework for Virtual Environments via Natural Language Agents and Its Application to Factory Automation

VEsNA, a Framework for Virtual Environments via Natural Language Agents and Its Application to Factory Automation

Bush Fire Simulation through Emotion-based BDI Methodology

Towards VEsNA, a Framework for Managing Virtual Environments via Natural Language Agents

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