Tree-Hillclimb Search: An Efficient and Interpretable Threat Assessment Method for Uncertain Battlefield Environments

Area of Science:

• Military science
• Artificial intelligence
• Decision support systems

Background:

• Effective battlefield decision-making requires rapid threat assessment in uncertain environments.
• Traditional analytical methods lack interpretability and struggle with complex causal relationships.
• Data-driven methods offer pattern discovery but suffer from a 'black-box' nature.

Purpose of the Study:

• To propose an efficient and interpretable threat assessment method for uncertain battlefield environments.
• To address the limitations of existing Bayesian network models in terms of expert experience constraints and complexity.
• To balance predictive accuracy and computational complexity for real-time battlefield applications.

Main Methods:

• Introduction of the Tree-Hillclimb Search method, a novel structure learning algorithm for Bayesian networks.
• Utilizing expert knowledge to constrain the initial network structure, guiding the discovery of causal dependencies.
• Refining the model under expert knowledge constraints to balance accuracy and complexity, validated by sensitivity analysis.

Main Results:

• The Tree-Hillclimb Search method demonstrates enhanced interpretability and high predictive accuracy.
• The method achieves high efficiency and real-time performance, crucial for dynamic battlefield conditions.
• Sensitivity analysis confirms model structure consistency with threat factor influence, supporting optimized sensor allocation.

Conclusions:

• The proposed method offers a significant advancement in threat assessment for uncertain battlefield environments.
• It provides a theoretical basis for hierarchical threat assessment and optimized resource allocation.
• The method exhibits good generality and broad applicability in military decision support systems.