Stratified tree search: a novel suboptimal heuristic search algorithm
Bibliographic record
Abstract
Traditional heuristic search algorithms use the ranking of states that a heuristic function provides to guide the search. In this paper—with the objective of improving suboptimality and runtime of search algorithms when only weak heuristics are available—we present Stratified Tree Search (STS), a suboptimal heuristic search algorithm that uses a heuristic to partition the state space to guide the search. We call this partition a type system. STS assumes that nodes of the same type will lead to solutions of the same cost. Thus, STS expands only one node of each type in every level of search. We show that in general STS offers a good tradeoff between solution quality and search speed by varying the size of the type system. However, in some cases, STS might not provide a fine adjustment of this tradeoff. We present a variant of STS, Beam STS (BSTS), that allows one to make fine adjustments of this tradeoff. BSTS combines the ideas of STS with those of Beam Search. Our empirical results in benchmark domains show that both STS and BSTS can find solutions of lower suboptimality in less time than standard heuristic search algorithms for finding suboptimal solutions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".