Optimal Average Case Strategy for Looking around a Corner
Bibliographic record
Abstract
A robot is free to move in a non-convex polygonal region, starting against an edge on the boundary. Ahead of the robot at one unit of distance is a corner, i.e. a reflex vertex (see Figure 1). The angle θ is unknown to the robot. The robot’s task is to look at the region around the corner, which it initially cannot see. We let ϕ = π − θ .I fϕ ≥ π/2, the robot is best off moving straight to the vertex. However, if ϕ is close to 0, much shorter paths exist, making this solution suboptimal. Therefore we ask: What is the best path for the robot to follow? In this paper, we look into the problem of finding an optimal average-case strategy under a homogeneous probability distribution for ϕ. The average-case performance of a strategy is measured by its average cost, defined as the expected value of the strategy’s competitive function. Given a value for ϕ, the competitive function of a strategy gives the ratio of the distance the robot travels to look around the corner (as prescribed by the strategy) to the shortest distance it must travel to do so. We give strong evidence that an optimal average-case strategy exists and achieves an average cost of ∼ 1.189.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 | 0.000 |
| 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".