Exploring Parallelization of Conjunctive Branches in Tableau-Based Description Logic Reasoning.
Bibliographic record
Abstract
Abstract. Multiprocessor equipment is cheap and ubiquitous now, but users of description logic (DL) reasoners have to face the awkward fact that the major tableau-based DL reasoners can make use only one of the available processors. Recently, researchers have started investigating how concurrent computing can play a role in tableau-based DL reasoning with the intention of fully exploiting the processing resources of multiprocessor computers. The published research mostly focuses on utilizing disjunctive branches, the or-part of tableau expansion trees. We investigated the possibility and the role of concurrently processing conjunctive branches, the and-part of tableau expansion trees. In this work, we present an algorithm to process conjunctive branches in parallel and address the key implementation aspects of the algorithm. A research prototype to execute this algorithm has been developed and empirically evaluated. The experimental results are presented and analyzed. We found that parallelizing the processing of conjunctive branches of tableau expansion trees is auspicious and can partly evolve into a scalable solution for DL reasoning. 1
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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 | 0.001 |
| 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".