Epistemic Plan Recognition
Bibliographic record
Abstract
The plan recognition task is to infer an actor's plan and goal given observations about its behavior. We submit that in some cases, for plan recognition to be effective and complete, it must appeal to a notion of epistemics to i) recognize epistemic goals, where the actor is trying to achieve some state of knowledge or belief; and ii) model the observer, and its knowledge of the actor, as first class elements of the plan recognition process. To this end, we formalize the notion of Epistemic Plan Recognition, which builds on two growing areas of research: epistemic planning and plan recognition. Our epistemic plan recognition specification appeals to an epistemic logic framework to represent agent beliefs. To realize our specification, we cast the epistemic plan recognition problem as an epistemic planning problem, whose solutions can be generated using existing epistemic planning tools. Finally, we evaluate our approach by utilizing and comparing existing epistemic planners on a diverse set of epistemic plan recognition problems.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".