Bibliographic record
Abstract
The distributed constraint satisfaction problem is a general formalization used to represent problems in distributed multi-agent systems. A large body of problems in artificial intelligence and computer science can be easily formulated as distributed constraint satisfaction problems. In this thesis we study agent ordering, effects of no-goods, search efficiency and threshold repairing in distributed constraint satisfaction problems and its variants. A summary of contributions is as follows: 1. We present a new algorithm, Dynamic Agent Ordering. A distinctive feature of this algorithm is that it uses the degree of unsatisfiability as a guiding parameter to dynamically determine agent ordering during the search. We show through an empirical study that our algorithm performs better than the existing approaches. In our approach, the independence of agents is guaranteed and agents without neighbouring relationships can run concurrently and asynchronously. (Part of this work was published in the Australian Al Conference (80)). 2. We extend the Dynamic Agent Ordering algorithm by incorporating a novel technique called nogood repairing. This results in a dramatic reduction in the nogoods being stored, and communication costs. In an empirical study, we11 show that this approach outperforms an equivalent static ordering algorithm and a current state-of-the-art technique in terms of execution time, memory usage and communication cost. (Part of this work was published at FLAIRS Conference (81)). Further, we introduce a new algorithm, Over-constrained Dynamic Agent Ordering, that breaks new ground in handling multiple variables per agent in distributed over-constrained satisfaction problems. The algorithm also uses the degree of unsatisfiability as a measure for relaxing constraints, and hence as a way to guide the search toward the best optimal solution(s). By applying our Threshold Repair method, we can solve a distributed constraint satisfaction problem without knowing whether the problem is under- or over-constrained. In an experimental study, we show that the new algorithm compares favourably to an implementation of asynchronous weak commitment search adapted to handle over-constrained problems. (Part of this work was published at the Canadian AI conference (79)).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".