Featured Argumentation Framework: Semantics and Complexity
Bibliographic record
Abstract
Dung's Argumentation Framework (AF) has been extended in several directions to make knowledge representation and reasoning tasks more intuitive and/or expressive. We present a novel extension of AF called Featured AF (FAF), where each argument has associated a set of features expressed by means of unary and binary facts. In such a context, a query is expressed by means of a conjunctive relational calculus formula which is evaluated over the extensions of the FAF. Then, this framework is further expanded into the so-called Extended FAF (EFAF), where a first-order logic formula (FOL) is used for reasoning over `feasible' subframeworks that satisfy the FOL formula and minimally differ from the original framework. We investigate the computational complexity of verification and acceptance problems under several semantics and show that incomplete AF (iAF) frameworks, including correlated iAF and constrained iAF, are special cases of EFAF.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".