A Tree-Hillclimb Search Model for Naval Air Defense Threat Assessment Integrating Expert Experience and Bayesian Networks

In dynamically evolving electromagnetic warfare scenarios, naval surface warships executing air defense operations must conduct real-time detection with precision to identify airborne targets, characterize their operational profiles (including type classification and mission modality), and implement rapid threat quantification. Bayesian networks, as probabilistic graphical models for representing and reasoning uncertainty, serve as effective tools for processing multivariate information. Compared with conventional methods, Bayesian networks demonstrate superior capability in managing uncertain information, while offering enhanced interpretability relative to black-box machine learning techniques such as neural networks. Traditional expert-informed Bayesian network models for naval air defense threat assessment, while operationally functional, exhibit critical limitations in explicating undocumented causal relationships among threat parameters and objectively quantifying causal dependencies between influential factors. To address these analytical constraints, this study presents a hybrid computational framework integrating two methodological advancements: first, the incorporation of structural learning algorithms to transcend purely experiential modeling, and second, the application of sensitivity analysis for impact metric quantification. The resultant Tree-Hillclimb Search model achieves synergistic integration of domain expertise and data-driven pattern discovery, thereby enhancing both the explanatory depth and quantitative rigor in naval threat assessment systems.