A Formal Definition of Object-Action Complexes and Examples at Different Levels of the Processing Hierarchy
Bibliographic record
Abstract
In this report the authors define and describe the concept of Object-Action Complexes and give some examples. OACs combine the concept of affordance with the computational efficiency of STRIPS. Affordance is the relation between a situation and the action that it allows. OACs are proposed as a framework for representing actions, objects and the learning process that constructs such representations at all levels. Formally, an OAC is defined as a triplet, composed of a unique ID, a predition function that codes the systems belief on how the world (which is defined as a kind of global attribute space) will change after applying the OAC and a statisical measure representing the success of an OAC. The prediction function is thereby a mapping within the global attribute space. The measurement captures the accuracy of this prediction function and describes the reliability of the OAC. Therefore, it can be used for optimal decision making, predicion of the outcome of a certain action and learning.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".