Planning with Action Abstraction and Plan Decomposition Hierarchies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Useful and suitable action representations, with accompanying planning algorithms are crucial for the task performance of many agent systems, and thus a core issue of research on intelligent agents. An efficient and expressive representation of actions and plans can allow planning systems to retrieve relevant knowledge faster and to access and use suitable actions more effectively. Two general approaches have been pursued in the past; STRIPS-based planners, which construct plans from scratch, based on primitive action descriptions and planners using pre-defined Plan Decompositions Hierarchies, also known as Hierarchical Task Networks. In our research, we integrated both an inheritance hierarchy of actions, using STRIPS-like action descriptions, with a plan decomposition hierarchy, which consists of pre-defined plan schemata. This combination is suitable for a richer action and plan representation, and thus an improved planning algorithm. We implemented and tested this approach for a prototypical example application: the travel planning domain.
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.
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 | 0.001 |
| Open science | 0.000 | 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 it