Evaluating state-space abstractions in extensive-form games
Bibliographic record
Abstract
Efficient algorithms exist for finding optimal policies in extensive-form games. However, human-scale problems are typically so large that this computation remains infeasible with modern computing resources. State-space abstraction techniques allow for the derivation of a smaller and strategically similar abstract domain, in which an optimal strategy can be computed and then used as a suboptimal strategy in the real domain. In this paper, we consider the task of evaluating the quality of an abstraction, independent of a specific abstract strategy. In particular, we use a recent metric for abstraction quality and examine imperfect recall abstractions, in which agents “forget ” previously observed information to focus the abstraction effort on more recent and relevant state information. We present experimental results in the domain of Texas hold’em poker that validate the use of distribution-aware abstractions over expectation-based approaches, demonstrate that the new metric better predicts tournament performance, and show that abstractions built using imperfect recall outperform those built using perfect recall in terms of both exploitability and one-on-one play.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 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".