Predicting Opponent's Production in Real-Time Strategy Games With Answer Set Programming
Bibliographic record
Abstract
The adversarial character of real-time strategy (RTS) games is one of the main sources of uncertainty within this domain. Since players lack exact knowledge about their opponent's actions, they need a reasonable representation of alternative possibilities and their likelihood. In this article we propose a method of predicting the most probable combination of units produced by the opponent during a certain time period. We employ a logic programming paradigm called Answer Set Programming, since its semantics is well suited for reasoning with uncertainty and incomplete knowledge. In contrast with typical, purely probabilistic approaches, the presented method takes into account the background knowledge about the game and only considers the combinations that are consistent with the game mechanics and with the player's partial observations. Experiments, conducted during different phases of StarCraft: Brood War and Warcraft III: The Frozen Throne games, show that the prediction accuracy for time intervals of 1–3 min seems to be surprisingly high, making the method useful in practice. Root-mean-square error grows only slowly with increasing prediction intervals—almost in a linear fashion.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".