Bibliographic record
Abstract
Software applications in Artificial Intelligence, particularly Natural Language Processing, often need to decide how far two given strings differ from each other in their content. To this day edit distance remains to be widely used for measuring the difference. Symbols in strings are compared, but the meanings of strings are not considered in almost all algorithms based on edit distance. This paper aims to define a logical formalism for comparing strings. Thus the comparisons are enhanced with computer-comprehensible semantics. More precisely, we propose SMAT, a String Matching Action Theory, written in the language of Situation Calculus. We show that SMAT can be used to flexibly represent various string operators. Damerau-Levenshtein edit operators are specifically used as an illustration example. We remark that 1) SMAT is, in addition, a software program implementation for string matching; 2) Knowledge-based heuristics in support of string-matching strategies can be easily incorporated into SMAT, and 3) SMAT provides new opportunities for string matching through automated planning in Artificial Intelligence.
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 | 0.008 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".