Bibliographic record
Abstract
Action windows—spatiotemporal regions enabling player's safe execution of key in-game actions—are foundational to game task planning, yet their automated generation remains underexplored. In stealth games, for example, level designers carefully create guard patrols and environment layouts. However, critical tasks such as planning assassination routes for high-value targets (VIPs) still depend heavily on manual tuning. This work formalizes VIP task planning as the problem of automatically generating a path through a predefined environment with guard patrols, such that VIP's path contains player's safe action windows that are temporally and spatially dispersed, while maintaining coherence and meaningful interactions with environmental elements. We introduce two approaches: (1) an evolutionary optimization approach that is efficient in generating diverse routes by balancing multiple objectives, and (2) a constraint-driven safe-block search method that guarantees optimal sequences under strict design thresholds. Initial experiments validate that the evolutionary method generates diverse, high-dispersion routes with rapid runtimes, whereas the safe-block approach enforces hard constraints with predictable performance. Both methods integrate directly with existing level and patrol data, offering scalable solutions for automated stealth mission generation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".