Building SASO Wargaming Simulations Without Programmers
Bibliographic record
Abstract
We have designed and prototyped a new software tool that will permit military planners to rapidly create wargaming systems customized for specific SASO missions without the assistance of a programmer. This tool (KAGES) possesses two major components: the authoring tool and the knowledge representation engine. The authoring component provides an intelligent, intuitive graphical user interface that can guide the user through the knowledge acquisition (KA) and simulation authoring process. By manipulating a palette of objects on a mission canvas, the user specifies the entities and domain knowledge necessary to fully describe a mission. KAGES is not simply a visual authoring tool, however. It collaborates with the user during authoring, drawing upon its built-in knowledge engineering expertise to extract the relevant information from the user and encode it. To help the user leverage the experience of past planners, KAGES also maintains a database of previously encoded domain knowledge from which it can dynamically retrieve and adapt elements to fit the current situational context. Of course, the system also allows advanced users to deactivate the intelligent assistance features and directly author missions in the underlying representation for maximum flexibility. In order to handle the complex data produced by the user interface, KAGES has at its core a knowledge representation engine designed for the codification of SASO domain knowledge. It is capable of managing all of the rules, facts, constraints, entities, and other elements that are pertinent to a particular mission, starting with METT-TP (Mission, Enemy, Troops, Terrain, Time, and Politics) and ranging all the way to social and cultural factors. The engine includes a compiler that can automatically generate wargaming scenarios from its internal knowledge structures, so that once a mission has been specified in KAGES, it can immediately be run as a simulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".