Efficient ADD operations for point-based algorithms
Bibliographic record
Abstract
During the past few years, point-based POMDP solvers have gradually scaled up to handle medium sized domains through better selection of the set of points and efficient backup methods. Point-based research has focused on flat, explicit representation of the state space, yet in many realistic domains a factored representation is more appropriate. The latter have exponentially large state-spaces, and current methods are unlikely to handle models of reasonable size. Thus, adapting point-based methods to factored representations by modeling propositional state spaces better, e.g. by using Algebraic Decision Diagrams (ADDs) is needed. While a straightforward ADD-based implementation can effectively tackle large factored POMDPs, we propose several techniques to further improve scalability. In particular, we show how ADDs can be used successfully in factored domains that exhibit reasonable locality. Our algorithms are several orders of magnitude faster than current point-based algorithms used with flat representations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".