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An adaptive operation planning and EBO-BPNN optimization method for decision support systems.

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This study introduces a novel course of action (COA) planning method using lines of operation (LOO) and artificial intelligence. The approach optimizes COA effect evaluation for enhanced combat decision-making support systems.

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Area of Science:

  • Artificial Intelligence
  • Operations Research
  • Military Science

Background:

  • Effective course of action (COA) formulation is critical for operational command.
  • Current research in command and control (C2) artificial intelligence focuses on intelligent decision-making support for COA.
  • Existing methods require optimization for efficiency in complex combat scenarios.

Purpose of the Study:

  • To propose a novel COA planning method based on lines of operation (LOO).
  • To develop an optimized effect evaluation model for COA using artificial intelligence.
  • To enhance the efficiency and reduce computational resource consumption in COA planning.

Main Methods:

  • Utilized Planning Domain Definition Language (PDDL) to model combat scenarios and COA.
  • Constructed an effect-based optimization (EBO) model for COA evaluation.
  • Employed dynamic Bayesian networks (DBNs) for initial effect evaluation.
  • Optimized the DBNs model using a backpropagation neural network (BPNN) for improved efficiency.

Main Results:

  • The LOO model successfully planned COA for a coordinated distributed air defense and anti-missile scenario.
  • The BPNN evaluation model achieved comparable results to the DBNs model with a Mean Absolute Percentage Error (MAPE) below 0.02%.
  • The BPNN model demonstrated a significant efficiency improvement of at least 65% compared to the DBNs model.

Conclusions:

  • This research presents the first modeled description of COA planning, automatic evaluation, and effect calculation optimization.
  • The developed method supports decision support systems (DSS) for military command and control.
  • The optimized approach offers practical application potential by reducing computational resource demands.