Mother Tree Optimization for Conditional Constraints and Qualitative Preferences
Bibliographic record
Abstract
The Constraint Satisfaction Problem (CSP) is a robust framework for representing and solving many challenging and complex problems under constraints. More specifically, a CSP includes a set of variables defined over discrete domains of values, and a set of constraints restricting the values that the variables can simultaneously take. Solving a CSP consists of finding a complete assignment of values to variables such that all the constraints are satisfied. Given that the CSP is an NP-complete problem, finding a feasible solution requires an exponential time cost in practice. To overcome this difficulty in practice, we have proposed a discrete version of our bio-inspired Mother Tree Optimization (MTO) method that we called Discrete MTO-CSP (DMTO-CSP). The Conditional CSP (CCSP) extends the CSP with variables added or removed, dynamically, following some activity constraints. CCSPs can be very relevant in many dynamic applications, such as configuration and planning, where the possible changes are known a priori and can be enumerated. In these applications, we often have to manage a set of constraints together with some users' preferences. This has motivated us to extend the CCSP model to qualitative preferences represented with the CP-net graphical model. We then propose an adapted variant of DMTO-CSP, that we call DMTO-CCSP, to solve CCSPs and CCSPs with preferences. In order to assess the performance of DMTO-CCSP, we conducted several comparative experiments on random CCSP instances generated using a variant of the known RB model. The results demonstrate the efficiency of DMTO-CSP compared to the known backtrack search technique.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".