A dynamic extension of the asynchronous weak-commitment search algorithm
Bibliographic record
Abstract
A current challenge in research is to deal with dynamically changing environments. This paper presents an algorithm to solve a dynamic distributed constraint satisfaction problem (dynamic DCSP) using a multi-agent system. The dynamic DCSP is a distributed CSP in which variables, values, and constraints are distributed among various agents, and those variables, values and constraints can be added to and removed from the system. Most real world applications can be mapped into a dynamic DCSP. The proposed algorithm is an extension of the asynchronous weak commitment search algorithm originally proposed by Yukoo (2001) designed to cope with the dynamically changing parameters of the problem. This paper presents a model for a dynamic DCSP and an agent system implementing the extended algorithm. To validate the algorithm, this paper applied it to a dynamic n-queens problem. The results show that the algorithm can successfully respond to changes to the size of the board and the number of queens, thus confirming its ability to deal with dynamic DCSPs
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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".