Multi‐criteria optimization of ball passing in simulated soccer
Bibliographic record
Abstract
Abstract Simulated soccer offers a standard real‐time environment for testing decision‐making methods for multi‐agent systems. One critical task is determining to which team‐mate the ball should be passed and the optimal point where this ball should be sent. Early methods based on enforced learning or heuristics tried to aggregate anticipated risks and pay‐offs instead of flexibly balancing them. That was because scholars were not treating ball passing as a multi‐criteria optimization problem in its classic sense. We propose a set of three criteria, tactical gain and two time balances, which should be balanced while selecting an optimal point on the field for passing the ball to. One of the advantages of this set of criteria is that they are treating direct and leading passes in the same way, thus offering a unified method for ball passing. In order to make this problem tractable, the continuous decision parameter space is replaced by a finite set of N points on the XY‐plane, which are carefully selected using some heuristics. This set is searched for non‐dominated alternatives, of which one alternative is further selected. The selection is based on the relative importance of the criteria supplied by the developer of the soccer agent. The selection method is original and uses sequential elimination of poor alternatives with respect to one criterion. Criteria are applied randomly, with the probabilities proportional to their relative importance. Experiments have shown that the multi‐criteria decision‐making algorithm is superior to its heuristic‐based counterpart. Furthermore, we suggest that it might be implemented in the RoboCup leagues dealing with physical robots. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".