Creating large numbers of game AIs by learning behavior for cooperating units
Bibliographic record
Abstract
We present two improvements to the hybrid learning method for the shout-ahead architecture for units in the game Battle for Wesnoth. The shout-ahead architecture allows for units to perform decision making in two stages, first determining an action without knowledge of the intentions of other units, then, after communicating the intended action and likewise receiving the intentions of the other units, taking these intentions into account for the final decision on the next action. The decision making uses two rule sets and reinforcement learning is used to learn rule weights (that influence decision making), while evolutionary learning is used to evolve good rule sets. Our improvements add knowledge about terrain to the learning and also evaluate unit behaviors on several scenario maps to learn more general rules. The use of terrain knowledge resulted in improvements in the win percentage of evolved teams between 3 and 14 percentage points for different maps, while using several maps to learn from resulted in nearly similar win percentages on maps not learned from as on the maps learned from.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".