Combining Influence Maps with Heuristic Search for Executing Sneak-Attacks in RTS Games
Why this work is in the frame
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Bibliographic record
Abstract
Real-Time Strategy (RTS) games have become a popular domain for AI research due to their large state and action spaces, as well as complex sub-problems. One popular strategy in RTS games is the idea of a "Sneak-Attack", in which one player attempts to sneak enemy units into the base of their opponent without being seen, in order to gain the element of surprise. In this paper we will present initial results on combining influence maps with heuristic search to produce a path-finding system which allows us to guide StarCraft drop ships in order to execute a sneak attack. Our preliminary results show that by combining these two techniques, we can efficiently and automatically produce paths that guide our drop ships in a stealthy manner toward the enemy base, minimizing distance traveled and avoiding enemy vision of our army.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it