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  • Professor Simon X. Yang

    Editor-in-Chief of Intelligence & Robotics
    Advanced Robotics & Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph, Guelph, ON, Canada.


  • Professor Hao Zhang

    Executive Editor of Intelligence & Robotics
    Department of control science and engineering,Tongji University.

    Hao Zhang is currently a distinguished professor at the Department of control science and engineering,Tongji University. Her research topics include: Networked control, Intelligent control, Advanced control theory and application, Vehicle and robot control, Complex dynamic network/Complex system modeling, Control and optimization, Time-delay system, Random system, Fuzzy control system analysis and integration, Multi-agent system, Distributed control and filtering, Smart grid, etc.


Dynamical decision-making (DDM) for multi-agent systems (MASs) in the presence of uncertainty is central to the field of artificial intelligence as well as many other fields, i.e., localization, programming, control, and optimization. The primary aim of DDM for MASs is to allow interactive agents to choose local action with private observation and adjust coupled-strategy accordingly. This is accomplished by suitable problem formulation and rational mechanism. The challenges are to design a framework able to resolve conflicts and obtain strategy under the model uncertainty and interaction uncertainty among agents. The conflicts resolution can happen with the help of Game Theory (GT). GT is an optimization-based framework that uses utility influenced by multiple actions/strategies. Learning is an important branch of Artificial Intelligence that provides agents the ability to automatically learn and improve strategy from the interaction. Learning solution to action/strategy plays the role of decision in various uncertainties. These learning algorithms may be applied to dynamical decision-making for multi-agent systems.



  • Introduction of Intelligence & Robotics
  • Hao Zhang: Learning-based dynamical decision-making for multi-agent systems
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