SR-APL: a model for a programming language for rational BDI agents with prioritized goals
Bibliographic record
Abstract
Recently, there has been much work on incorporating declarative goals in Belief-Desire-Intention Agent Programming Languages (e.g. [3]). In a BDI APL with declarative goals (APLwDG), declarative goals are used essentially for monitoring goal achievement and performing recovery when a plan has failed, performing rational deliberation, and reacting in a rational way to changes in goals that result from communication. While APLwDGs have evolved over the past few years, to keep them tractable and practical, they sacrifice some principles of rationality. In particular, while selecting plans to achieve a declarative goal, they ignore other concurrent intentions of the agent. As a consequence, the selected plans may be inconsistent with other intentions. Also, these APLwDGs typically rely on syntactic formalizations of declarative goals, whose properties are often not well understood.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".