Kenniscompilatie en tellen: een algebraïsche reis
Bibliographic record
Abstract
The journey captured by this dissertation centers around knowledge compilation, model counting, and their role within state-of-the-art inference algorithms for probabilistic logic programming (PLP) languages. Model counting is the task of finding the number of solutions that satisfy a given set of constraints such as 'A or not B, and C'. Knowledge compilation techniques can reformulate these constraints such that counting becomes easier. This dissertation has four main contributions in this domain: 1) It introduces a novel improvement to model counters that enhances their performance by exploiting symmetries present within the constraints. 2) It demonstrates the general applicability of the algebraic model counting (AMC) variant, through its use within a decision making under uncertainty setting, and a synthesis of the 15-year ProbLog journey and the resulting insight that several PLP frameworks are unifiable under the same algebraic counting framework. 3) It analyzes the impact of the variable integration order on weighted model integration tasks, a counting variant that involves continuous variables, and proposes several novel ordering heuristics that significantly reduce the model integration run time. 4) It contributes to laying the foundations for knowledge compilation with respect to a background theory, which allows the use of constraints that go beyond Boolean variables, including arithmetic constraints.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".