This study aims to develop a quantum computing-based neurocognitive architecture that allows an agent to perform autonomous behaviors.Therefore,we present a brain-inspired cognitive architecture for autonomous agents ...This study aims to develop a quantum computing-based neurocognitive architecture that allows an agent to perform autonomous behaviors.Therefore,we present a brain-inspired cognitive architecture for autonomous agents that integrates a prefrontal cortex-inspired model with modern deep learning(a transformer-based reinforcement learning module)and quantum algorithms.In particular,our framework incorporates quantum computational routines(Deutsch-Jozsa,Bernstein-Vazirani,and Grover’s search)to enhance decision-making efficiency.As a novelty of this research,this comprehensive computational structure is empowered by quantum computing operations so that superiority in speed and robustness of learning compared to classical methods can be demonstrated.Another main contribution is that the proposed architecture offers some features,such as meta-cognition and situation awareness.The meta-cognition aspect is responsible for hierarchically learning sub-tasks,enabling the agent to achieve the master goal.The situation-awareness property identifies how spatial-temporal reasoning activities related to the world model of the agent can be extracted in a dynamic simulation environment with unstructured uncertainties by quantum computation-based machine learning algorithms with the explainable artificial intelligence paradigm.In this research,the Minecraft game-based simulation environment is utilized for the experimental evaluation of performance and verification tests within complex,multi-objective tasks related to the autonomous behaviors of a smart agent.By implementing several interaction scenarios,the results of the system performance and comparative superiority over alternative solutions are presented,and it is discussed how these autonomous behaviors and cognitive skills of a smart agent can be improved in further studies.Results show that the quantum-enhanced agent achieves faster convergence to an 80%task 2×success rate in exploration tasks and approximately 15%higher cumulative rewards compared to a classical deep RL baseline.These findings demonstrate the potential of quantum algorithms to significantly improve learning and performance in cognitive agent architectures.However,advantages are task-specific and less pronounced under high-uncertainty,reactive scenarios.Limitations of the simulation environment are acknowledged,and a structured future research roadmap is proposed involving highfidelity simulation validation,hardware-in-the-loop robotic testing,and integration of advanced hybrid quantum-classical architectures.展开更多
The efficient and reliable human centered design of products and processes is a major goal in manufacturing industries for numerous human factors must be taken into account during the entire life cycle of products. A ...The efficient and reliable human centered design of products and processes is a major goal in manufacturing industries for numerous human factors must be taken into account during the entire life cycle of products. A multi-agents intelligent design system is presented for manufacturing process simulation and products＇ ergonomic analysis. In virtual design environment, the virtual human with high-level intelligence performs tasks＇ operation autonomously and shows optimum posture configuration with ergonomic assessment results in real time. The functions are realized by intelligent agents architecture based on a modem approach derived from fuzzy multi-objects decision-making theory. A case study is presented to demonstrate the feasibility of the suggested methodology.展开更多
Creating realistic virtual humans has been a challenging objective in computer science research for some time. This paper describes an integrated framework for modeling virtual humans with a high level of autonomy. Th...Creating realistic virtual humans has been a challenging objective in computer science research for some time. This paper describes an integrated framework for modeling virtual humans with a high level of autonomy. The framework seeks to reproduce human-like believable behavior and movement in virtual humans in a virtual environment. The framework includes a visual and auditory information perception module, a decision network based behavior decision module, and a hierarchical autonomous motion control module. These cooperate to model realistic autonomous individual behavior for virtual humans in real-time interactive virtual environments. The framework was tested in a simulated virtual environment system to demonstrate the ability of the framework to create autonomous, perceptive and intelligent virtual humans in real-time virtual environments.展开更多
文摘This study aims to develop a quantum computing-based neurocognitive architecture that allows an agent to perform autonomous behaviors.Therefore,we present a brain-inspired cognitive architecture for autonomous agents that integrates a prefrontal cortex-inspired model with modern deep learning(a transformer-based reinforcement learning module)and quantum algorithms.In particular,our framework incorporates quantum computational routines(Deutsch-Jozsa,Bernstein-Vazirani,and Grover’s search)to enhance decision-making efficiency.As a novelty of this research,this comprehensive computational structure is empowered by quantum computing operations so that superiority in speed and robustness of learning compared to classical methods can be demonstrated.Another main contribution is that the proposed architecture offers some features,such as meta-cognition and situation awareness.The meta-cognition aspect is responsible for hierarchically learning sub-tasks,enabling the agent to achieve the master goal.The situation-awareness property identifies how spatial-temporal reasoning activities related to the world model of the agent can be extracted in a dynamic simulation environment with unstructured uncertainties by quantum computation-based machine learning algorithms with the explainable artificial intelligence paradigm.In this research,the Minecraft game-based simulation environment is utilized for the experimental evaluation of performance and verification tests within complex,multi-objective tasks related to the autonomous behaviors of a smart agent.By implementing several interaction scenarios,the results of the system performance and comparative superiority over alternative solutions are presented,and it is discussed how these autonomous behaviors and cognitive skills of a smart agent can be improved in further studies.Results show that the quantum-enhanced agent achieves faster convergence to an 80%task 2×success rate in exploration tasks and approximately 15%higher cumulative rewards compared to a classical deep RL baseline.These findings demonstrate the potential of quantum algorithms to significantly improve learning and performance in cognitive agent architectures.However,advantages are task-specific and less pronounced under high-uncertainty,reactive scenarios.Limitations of the simulation environment are acknowledged,and a structured future research roadmap is proposed involving highfidelity simulation validation,hardware-in-the-loop robotic testing,and integration of advanced hybrid quantum-classical architectures.
文摘The efficient and reliable human centered design of products and processes is a major goal in manufacturing industries for numerous human factors must be taken into account during the entire life cycle of products. A multi-agents intelligent design system is presented for manufacturing process simulation and products＇ ergonomic analysis. In virtual design environment, the virtual human with high-level intelligence performs tasks＇ operation autonomously and shows optimum posture configuration with ergonomic assessment results in real time. The functions are realized by intelligent agents architecture based on a modem approach derived from fuzzy multi-objects decision-making theory. A case study is presented to demonstrate the feasibility of the suggested methodology.
基金Supported by the National Natural Science Foundation of China(No.60801053)the Beijing Natural Science Foundation (No.4082025)+4 种基金the Doctoral Foundation of China (No.20070004037)the Fundamental Research Funds for the Central Universities (Nos.2009JBM135 and 2011JBM023)the BJTU Hongguoyuan Innovative Talent Program (No.151139522)the Beijing Excellent Doctoral Thesis Program (No.YB20081000401)the National Key Basic Research and Development (973) Program of China (No.2006CB303105)
文摘Creating realistic virtual humans has been a challenging objective in computer science research for some time. This paper describes an integrated framework for modeling virtual humans with a high level of autonomy. The framework seeks to reproduce human-like believable behavior and movement in virtual humans in a virtual environment. The framework includes a visual and auditory information perception module, a decision network based behavior decision module, and a hierarchical autonomous motion control module. These cooperate to model realistic autonomous individual behavior for virtual humans in real-time interactive virtual environments. The framework was tested in a simulated virtual environment system to demonstrate the ability of the framework to create autonomous, perceptive and intelligent virtual humans in real-time virtual environments.
