Robust shortest path with local information revelation
Bibliographic record
Abstract
In this thesis, a robust path planning method is developed to find optimal path traversal policies in an uncertain environment.This method restricts itself to uncertainty in discrete sets rather than distributions for the uncertain costs.Additionally, the uncertainty of each edge does not remain independent, as a set of possible true worlds is known.This formulation allows for the creation of feasible sets of possible true worlds.As information is revealed about the true state of the world, the elements inside the feasible set are reduced until only one possible true world remains.This state and information revelation is similar to a Partially Observable Markov Decision Process (POMDP) structure.The solution developed in this thesis shows that Value Iteration (VI) in infinite horizons can have guarantees to converge to the optimal solution.The complication of this method is that as the number of possible states grows in relation to the size of the environment and the uncertainty in the world, the computational requirements grow exponentially.To allow for a Robust Shortest Path (RSP) to be found in these cases, we show how a Monte Carlo Tree Search (MCTS) method can be used.This method builds on previous examples in the literature, which use MCTS in games such as Chess and Go.Furthermore, in larger worlds, certain nodes and edges can be pruned if they cannot be used along an optimal path.This pruning can reduce the computational requirements in both the VI algorithm and the MCTS method.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".