A Divide and Conquer Algorithm for Dominance Testing in Acyclic CP-Nets
Bibliographic record
Abstract
The Conditional Preference Network (CP-net) represents user's conditional ceteris paribus (all else being equal) preference statements in a graphical manner. In general, an acyclic CP-net induces a strict partial order over the outcomes. The task of comparing two outcomes (dominance testing) is generally PSPACE-complete, which is a limitation for this intuitive model, especially when representing and solving preference-based constrained optimization problems. In order to overcome this limitation in practice, we propose a divide and conquer algorithm that compares two outcomes according to dominance testing. The algorithm divides the original CP-net into sub CP-nets, and recursively calls itself for each of the sub CP-nets until it reaches to a termination criterion. In the termination criterion, the answer of the dominance query is returned. With a theoretical analysis of the time performance, we demonstrate that the proposed algorithm outperforms the existing methods.
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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".