Cooperative Multi-Agent Systems Cognitive Modeling

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    Abstract

    The project aims to develop models for analyzing artificial intelligence (AI) robots’ (AI agents) motivations and behaviors and to understand their diverse relationships as they cooperate and adapt to the needs and behaviors of humans and other AI agents. The project’s novelty is its focus on modeling cooperative multi-agent systems (MAS) from the cognitive science perspective and investigating how they reach consensus and integrate human needs through a shared needs-oriented trust network in the interaction. The project’s impacts are significant because the proposed cooperative MAS models will help artificial social systems (like multi-robot systems and self-driving cars) integrate into human society and work harmoniously with us, supporting sustainable human development. Moreover, the success of this project could enable cognitive modeling for cooperability-aware MAS of advanced AI architectures and software, leading to new technologies and applications in the computing, communications, electronics, aerospace, transportation, agriculture, and defense industries. It will have the potential to revolutionize AI and Robotics technology.

    NSF Support Link for More Details: FRR: Cooperative Multi-Agent Systems Cognitive Modeling

Bayesian Strategy Network based Reinforcement Learning

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    A Reinforcement Learning Model based on the Bayesian Strategy Network for Robot Locomotion & Planning

    The proposed research aims to develop a new reinforcement learning (RL) model based on Bayesian Strategy Network (BSN) for robot locomotion and planning. By combining AI and cognitive robotics technology, the model can support robots developing diverse strategies and skills to adapt to complex environments and achieve various tasks efficiently.

    Objective: A cognitive robotic model for robot locomotion and planning. This research will develop a novel cognitive robotic model based on BSN and Deep RL architecture to improve the convergent speed and sample efficiency. Furthermore, we will implement our model in a real robot, such as Unitree Go2 robot dog, to achieve dynamic and complex tasks.

    Reference Paper:Bayesian Strategy Networks Based Soft Actor-Critic Learning

Edge Computing based Human-Robot Cognitive Fusion

Innate-Values-driven Reinforcement Learning (IVRL)

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