Planning under uncertainty as G<scp>OLOG</scp>programs
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Bibliographic record
Abstract
A number of logical languages have been proposed to represent the dynamics of the world. Among these languages, the Situation Calculus (McCarthy and Hayes 1969 McCarthy J. Hayes P. J. 1969 Some philosophical problems from the standpoint of artificial intelligence In B. Meltzer and D. Michie (eds) Machine Intelligence 4 Edinburgh Edinburgh University Press pp. 463–502 [Google Scholar]) has gained great popularity. The GOLOG programming language (Levesque et al. 1997 Levesque, H. J., Reiter, R., Lespérance, Y., Lin, F. and Scherl, R. B. 1997. Golog: logic programming languge for dynamic domains. The Journal of Logic Programming, 31: 59–84. [Crossref], [Web of Science ®] , [Google Scholar], Giacomo et al. 2000 Giacomo G. D. Lespérance Y. Levesque H. 2000 ConGolog, a concurrent programming language based on the situation calculus: foundations Artificial Intelligence 121 1–2 109 169 Available online: http://www.cs.toronto.edu/cogrobo/Papers/ConGologLang.ps.gz [Crossref] , [Google Scholar]) has been proposed as a high-level agent programming language whose semantics is based on the Situation Calculus. For efficiency reasons, high-level agent programming privileges programs over plans; therefore, GOLOG programs do not consider planning. This article presents algorithms that generate conditional GOLOG programs in a Situation Calculus extended with uncertainty of the effects of actions and complete observability of the world. Planning for contingencies is accomplished through two kinds of plan refinement techniques. The refinement process successively increments the probability of achievement of candidate plans. Plans with loops are generated under certain conditions.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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