Abstract:

Achieving expert-level performance in simulation-based training relies on creating complex, adaptable scenarios—a traditionally laborious and resource-intensive process. Although prior research explored scenario generation for military training, pre-LLM AI tools struggled to generate sufficiently complex or adaptable scenarios. This paper introduces a multi-agent, multi-modal reasoning framework that leverages Large Language Models (LLMs) to generate critical training artifacts, such as Operations Orders (OPORDs). Our approach automates and enhances AI-assisted scenario generation, significantly reducing manual workload and accelerating development. We structure our framework by decomposing scenario generation into a hierarchy of subproblems, and for each one, defining the role of the AI tool: (1) generating options for a human author to select from, (2) producing a candidate product for human approval or modification, or (3) generating textual artifacts fully automatically. Our framework employs specialized LLM-based agents to address distinct subproblems. Each agent receives input from preceding subproblem agents — integrating both text-based scenario details and visual information (e.g., map features, unit positions) — and applies specialized reasoning to produce appropriate outputs. Subsequent agents process these outputs sequentially, preserving logical consistency and ensuring accurate document generation. Such a multi-agent strategy overcomes the limitations of basic prompting or single-agent approaches when tackling highly complex tasks. We validate our framework through a proof-of-concept that generates the scheme of maneuver and movement section of an OPORD while estimating map positions and movements as a precursor — demonstrating its feasibility and accuracy. Our results demonstrate the potential of LLM-driven multi-agent systems to generate coherent, nuanced documents and adapt dynamically to changing conditions — advancing automation in scenario generation for military training.

Keywords: AUTOMATION;MACHINE LEARNING;SCENARIOS

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Matthew Green

Biography