The accelerating pace of modern conflict—characterized by extended sensor ranges, increased weapon system standoff distances, and expanded operational theaters—has fundamentally challenged traditional manned planning processes. CoA generation, the core cognitive task of military decision-making, now demands computational assistance to maintain decision velocity. While defense organizations globally pursue AI-driven planning automation, the opacity surrounding these systems creates significant gaps in understanding their technical maturity and architectural foundations.
This research addresses that gap by systematizing the CoA planning pipeline through the lens of established military doctrine. The approach decomposes planning into discrete stages, each amenable to specific AI methodologies. Early stages emphasizing situation analysis and threat assessment leverage machine learning for sensor fusion and pattern recognition across multi-modal intelligence data. Intermediate planning phases benefit from constraint satisfaction and graph-based search algorithms, enabling exploration of feasible action sequences within operational constraints. Terminal stages, focused on plan evaluation and recommendation, employ decision support models that integrate probabilistic reasoning with domain-specific heuristics.
The proposed architecture integrates three principal components: a perception module for real-time environmental modeling, a planning engine implementing hierarchical task decomposition, and an evaluation framework that ranks candidate courses of action against multi-objective criteria. The planning engine employs temporal reasoning to manage sequential dependencies and maintains constraint networks representing operational limitations. Critically, the architecture preserves human-in-the-loop decision authority, with AI systems generating and ranking options rather than executing autonomous decisions.
By grounding automation in documented military doctrine while leveraging contemporary AI advances, this framework provides both practitioners and researchers with a systematic foundation for developing transparent, auditable planning systems. The work emphasizes that effective military automation requires tight integration of domain knowledge, mathematical rigor, and human-centered design principles.
