Changes’05 International Workshop on Constraint Solving under Change and Uncertainty
Bibliographic record
Abstract
Problem solving under change and uncertainty is a significant issue for many practical applications. Solutions must be obtained before the full problem is known, and often must be executed as the environment changes. Many of the application areas have been tackled by constraint based methods, but current constraint solving tools offer little support for uncertain dynamic problems. Possible enhancements could include rapid reaction to problem changes, robust solutions, prediction of future changes and contingent solutions, time guarantees or exploitation of known time limits. This workshop will continue the series of CP workshops on online solving, change and uncertainty, and in particular following on from Changes'04 held at Toronto during CP2004. The workshop aims to bring together researchers interested in the general topic, to consider how existing techniques can be enhanced, and to explore combinations of different techniques. This year, submission was by position paper only – this was intended to encourage participation and discussion at the workshop. All position papers are included in these working notes. We are very pleased to have an invited talk by Rina Dechter, on current
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".