On Using Action Inheritance and Modularity in PDDL Domain Modelling
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
The PDDL modelling problem is known to be challenging, time consuming and error prone. This has led researchers to investigate methods of supporting the modelling process. One particular avenue is to adapt tools and techniques that have proven useful in software engineering to support the modelling process. We observe that concepts, such as inheritance and modularity have not been fully explored in the context of modelling PDDL planning models. Within software engineering these concepts help to organise and provide structure to code, which can make it easier to read, debug, and reuse code. In this work we consider inheritance and modularity and their use in PDDL action descriptions, and how these can have a similar impact on the PDDL modelling process. We define an extension to PDDL and develop appropriate tools to compile models using these extensions, both directly from the command line and through the Visual Studio Code PDDL extension. We report on our use of inheritance and modularity when modelling a planning model for a companion robot scenario. We also discuss the benefits of exploiting the inheritance hierarchy in other modules within our robot system.
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 | 0.000 |
| 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