Planning with qualitative temporal preferences
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
In this paper, we address the problem of specifying and generating preferred plans using rich, qualitative user preferences. We propose a logical language for specifying non-Markovian preferences over the evolution of states and actions associated with a plan. The semantics for our first-order preference language is defined in the situation calculus. Unlike other recent temporal preference languages, our preferences are qualitative rather than just ordinal, affording greater expressivity and less incomparability. We propose an approach to computing preferred plans via bounded best-first search in a forwardchaining planner. Key components of our approach are the exploitation of progression to efficiently evaluate levels of preference satisfaction over partial plans, and development of an admissible evaluation function that establishes the optimality of best-first search. We have implemented our planner PPLAN and evaluated it experimentally. Our preference language and planning approach is amenable to integration with several existing planners, and beyond planning, can be used to support arbitrary dynamical reasoning tasks involving preferences.
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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