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    This study introduces a novel multilayer network for efficient path planning in mazes. The network solves complex pathfinding problems on large mazes quickly, without requiring network training.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Network Science

    Background:

    • Path planning is a critical problem across many applications.
    • Existing algorithms like BFS and Dijkstra's can be computationally intensive for large-scale problems.
    • Efficiently solving path planning for multiple start and endpoints remains a challenge.

    Purpose of the Study:

    • To develop a highly efficient method for solving path planning problems in mazes.
    • To demonstrate the efficacy of novel multilayer networks for complex pathfinding.
    • To provide a solution that is computationally less demanding than traditional algorithms.

    Main Methods:

    • Utilized a novel configuration of multilayer networks.
    • Employed only weighted pooling operations within the networks.
    • No network training was required for the proposed method.

    Main Results:

    • The networks generated path planning solutions identical to classical algorithms (BFS, Dijkstra's, TD(0)).
    • Successfully solved very large mazes (nearly one billion nodes) with dense obstacles.
    • Handled several thousand importance-weighted path endpoints efficiently.

    Conclusions:

    • The proposed multilayer network offers a highly efficient solution for complex path planning.
    • This approach enables rapid solving of large-scale maze problems in a single pass on parallel hardware.
    • The method bypasses the need for network training, simplifying implementation and reducing computational cost.