A semantic-based flexible framework for automatic behavior composition
Bibliographic record
Abstract
The behavior composition problem consists in the synthesis of a controller that coordinates a set of available behaviors with the aim of realizing a desired target behavior. This problem suffers from a serious drawback because, for many instances, the target behavior cannot be achieved. In that case, the user is responsible for bringing changes until the problem becomes solvable. This inherent limitation is due to the fact that an exact match is required between the actions initiated by the user and those offered by the available behaviors. The main question is, how can a controller select a suitable behavior such that the functionality of one of its available actions is close to that currently requested by the desired target behavior? This paper provides an answer to this question by associating semantics to actions based on expectations and exploiting a semantic similarity function between actions. Similar actions that have different names, but comparable tasks, are considered equivalent. This renders the behavior composition framework more flexible so that it can fit with semantic environments.
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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 | 0.000 | 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 | 0.001 | 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 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".