The huddle: Combining AI techniques to coordinate a player's game characters
Bibliographic record
Abstract
We present the huddle, a concept for extending games in which the player is responsible for a group of game characters. The huddle combines several AI methods to allow the player to create a cooperative strategy for his characters to solve a scenario of the game and it takes away from the player the need to frantically jump around in controlling his characters to employ the strategy idea he has. The huddle is entered from a saved game state and allows the player to provide his characters with strategy ideas in form of situations and the actions he wants the characters to take (SAPs). A learner then uses these ideas and adds to it additional SAPs to create a complete strategy. The learner uses a simulation of the real game that uses models for the nonplayer characters based on the experiences the player had with the game, so far, to evaluate strategy candidates. We evaluated the huddle idea with a fantasy-themed role playing game and show that the huddle indeed allows a player to concentrate on his strategy while still requiring him to come up with the solution ideas for scenarios.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".