Foundations for generalized planning in unbounded stochastic domains
Why this work is in the frame
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Bibliographic record
Abstract
Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Generalized plans, such as plans with loops, are widely used in AI. Among other things, they are straightforward to execute, they allow action repetition, and they solve multiple problem instances. However, the correctness of such plans is non-trivial to define, making it difficult to provide a clear specification of what we should be looking for. Proposals in the literature, such as strong planning, are universally adopted by the community, but were initially formulated for finite state systems. There is yet to emerge a study on the sensitivity of such correctness notions to the structural assumptions of the underlying plan framework. In this paper, we are interested in the applicability and correctness of generalized plans in domains that are possibly unbounded, and/or stochastic, and/or continuous. To that end, we introduce a generic controller framework to capture different types of planning domains. Using this framework, we then study a number of termination and goal satisfaction criteria from first principles, relate them to existing proposals, and show plans that meet these criteria in the different types of domains.
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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 | 0.001 | 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 | 0.001 | 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 it