Asymmetric abstractions for adversarial settings
Bibliographic record
Abstract
In multiagent domains, an agent's beliefs about how other agents will or could act plays a significant role in their own behaviour. In large domains where it is infeasible to uniquely represent every possible decision an agent will face, abstraction is often used to collapse the state and action space to make the problem tractable. By abstracting other agents' views of the environment, the agent makes assumptions about how other agents act. Incorrect abstraction choices can yield less than ideal performance as other agents may, in reality, use coarser or finer abstraction than they were modelled with. The standard approach when abstracting is to use symmetric abstraction: where all agents are assumed to distinguish states in the same way. This work examines the benefits and potential pitfalls of using asymmetric abstractions in two-player zero-sum extensive-form games. Using the domain of two-player limit Texas hold'em poker, we investigate the performance of strategies using both symmetric and asymmetric abstractions in terms of in-game utility and worst-case utility in the real game. Furthermore, we show that combining asymmetric abstractions with robust counter-strategy techniques can produce counter-strategies which dominate their symmetric abstraction counterparts in terms of both exploitative power and worst-case utility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".