Deception in A Multi-agent Adversarial Game: The Game of Guarding Several Territories
Bibliographic record
Abstract
In this paper, a deceitful behaviour in an adversarial game is investigated. The simulation platform is the game of guarding several territories. In the game, an invader is playing in front of two defenders. A two-level policy system is proposed in this paper. The first level is called lower-level policy, which is the policy to invade or defend a particular territory. The second level is called higher level-policy, which decides about the invader's behaviour and defenders' belief. The invader tries deceiving the defenders by repeatedly changing its goal from a fake goal to the true goal. On the other side, the defenders try to guess the goal by the invader's movement. The lower-level policy is trained via the FACL algorithm, while the higher-level policy is derived via the genetic algorithm. The results show a significant improvement in the invader's pay-off by behaving deceitfully in the game.
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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 | 0.000 | 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) | 0.000 | 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 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".