While the ball in the digital soccer is rolling, where the non-player characters should go in a defensive situation?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The non-player characters (NPCs), i.e. the artificial characters that are not under direct control by the user, are essential part of many digital games. Achieving the realistic behavior by NPCs in digital sports games such as the simulated soccer is challenging. Here we limit our scope to the defensive situation, i.e. when the ball is controlled by the opponents, and propose a systematic method for optimal NPC positioning. So far the methods for automatically finding defensive positions by the intelligent robotic soccer players have been investigated by some scholars within RoboCup, an international research and educational initiative in Artificial Intelligence and robotics. Although simulated soccer teams using these methods have proved to be reasonably good, the collaboration issue in defensive situations has been overlooked. In this paper we propose a systematic approach based on solving a multi-criteria assignment problem. This allows gracefully balancing the costs and rewards involved in defensive positioning to achieve better results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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