Deception In The Game of Guarding Multiple Territories: A Machine Learning Approach
Bibliographic record
Abstract
In this paper, a deceptive version of guarding a territory in a grid world is proposed. Like the original version, a defender tries to intercept an invader before it invades the targets. However, the discerning invader can deceive the defender about its real goal so that it can improve its performance. On the other hand, the defender tries to confront the invader by guessing its true goal. A two-level policy is obtained via reinforcement learning (RL). In the lower level, the invader and the defender learn their optimal policies to invade or defend a particular territory. In the higher level, the invader learns which territory it should pretend to invade in order to manipulate the defender's belief function. A multiagent reinforcement learning (MARL) algorithm is implemented for obtaining the optimal policies via the minimax Q-learning algorithm at the lower level. Whereas for the higher-level policy a single-agent Q-learning algorithm is utilized. Results of different reward functions are compared. The results show that the invader can improve its performance by taking advantage of deception.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".