Mean Field Theory in Doing Logic Programming Using Hopfield Network
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Bibliographic record
Abstract
Logic program and neural networks are two important perspectives in artificial intelligence. Logic describes connections among propositions. Moreover, logic must have descriptive symbolic tools to represent propositions. Meanwhile representation of neural networks on the other hand is in non-symbolic form. The objective in performing logic programming revolves around energy minimization is to reach the best global solutions. On the other hand, we usually gets local minima solutions also. In order to improve this, based on the Boltzmann machine concept, we will derive a learning algorithm in which time-consuming stochastic measurements of collerations are replaced by solutions to deterministic mean field theory (MFT) equations. The main idea of mean field algorithm is to replace the real unstable induced local field for each neuron in the network with its average local field value. Then, we build agent based modelling (ABM) by using Netlogo for this task.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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