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This summary is machine-generated.

This study presents a novel plugin framework for multi-agent systems, combining symbolic and connectionist AI approaches for robust automated task planning in dynamic environments. Experiments with unmanned surface vehicles confirm successful task execution.

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cooperative–competitive teamingmulti agent reinforcement learningneuro-symbolicplanning domain definition languagetask planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Multi-agent Systems

Background:

  • Increasing demand for autonomous robots in dynamic environments.
  • Need for advanced automated task planning in multi-agent systems.
  • Limitations of purely symbolic or connectionist approaches in complex scenarios.

Purpose of the Study:

  • To introduce a plugin framework for multi-robot task planning.
  • To integrate symbolic and connectionist AI approaches for enhanced planning capabilities.
  • To enable robots to execute tasks in wide and dynamic environments.

Main Methods:

  • Developed a plugin framework integrating symbolic and connectionist AI.
  • Utilized Planning Domain Definition Language (PDDL) for symbolic planning.
  • Employed cooperative-competitive reinforcement learning for connectionist planning.
  • Validated the architecture through simulations and experiments with unmanned surface vehicles.

Main Results:

  • The proposed framework successfully enabled multi-robot task planning.
  • Demonstrated effective task execution in wide and dynamic environments.
  • Verified the combined approach's superiority over individual methods through simulations.

Conclusions:

  • The integrated symbolic and connectionist framework provides a robust solution for multi-agent automated task planning.
  • The system is capable of handling complex tasks in dynamic, real-world environments.
  • Experimental validation with unmanned surface vehicles confirms the practical applicability and success of the proposed approach.