Topology-Driven Solver Selection for Stochastic Shortest Path MDPs via Explainable Machine Learning
Bibliographic record
Abstract
Selecting optimal solvers for complex AI tasks grows increasingly difficult as algorithmic options expand. We address this challenge for Stochastic Shortest Path Markov Decision Processes (SSP-MDPs) - a core model for robotics navigation, autonomous system planning, and stochastic scheduling - by introducing a topology-driven solver selection framework. First, we identify and empirically validate topological features - including strongly connected components, goal-state ratio, goal eccentricity (i.e., maximal distance to a goal), and average actions per state - that critically influence solver performance across synthetic and real-world SSP-MDPs. Using these insights, we propose the first classifier able to predict the fastest MDP solver for a given instance, achieving over 64\% accuracy on diverse benchmarks. Counterfactual explainability analysis further clarifies how these features govern solver efficiency. By directly linking topological structures to algorithmic performance, our work streamlines solver selection while enhancing computational efficiency, offering a principled approach to MDP optimization.
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".