On Utilizing the Pursuit Paradigm to Enhance the Deadlock-Preventing Object Migration Automaton
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the most common problems encountered in computing is that of "partitioning", and probably the most reputed solution for partitioning is the Object Migration Automata (OMA). The OMA has proven applications in databases, attribute partitioning, processor-based assignment etc. However, one of the known deficiencies of the OMA is an internal deadlock scenario which is discussed in this paper. This occurs when the problem size is large, i.e., the number of objects and partitions are large, and when the probability of receiving a reward (i.e., one that "strengthens" the current partitioning), from the Environment is not significant. As a result of this, it can take the OMA a considerable number of iterations to recover from an inferior configuration. This property, that characterizes Learning Automaton (LA) in general, is especially true for the OMA-based methods. In spite of the fact that various solutions have been proposed to remedy this issue for general families of LA, overcoming this hurdle is a completely unexplored area of research for conceptualizing how the OMA should interact with the Environment. Indeed, the best reported version of the OMA, the Enhanced OMA (EOMA), has been proposed to mitigate the consequent deadlock scenario. In this paper, we demonstrate that the incorporation of the intrinsic properties of the Environment into the OMA's design leads to a higher learning capacity, and to a more consistent partitioning. To achieve this, we incorporate the state-of-the-art pursuit principle utilized in the field of LA by estimating the Environments reward/penalty probabilities, and use them to further augment the EOMA. We also verify the performance of our proposed method, referred to as the Pursuit EOMA (PEOMA), through simulation, and demonstrate a significant increase in the convergence rate, i.e., sometimes by a factor of as large as forty. It also yields a noticeable reduction in sensitivity to the noise in the Environment.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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 it