Toward optimizing static target search path planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Discrete static open-loop target search path planning is known to be a NP (non-deterministic polynomial) - Hard problem, and problem-solving methods proposed so far rely on heuristics with no way to properly assess solution quality for practical size problems. Departing from traditional nonlinear model frameworks, a new integer linear programming (ILP) exact formulation and an approximate problem-solving method are proposed to near-optimally solve the discrete static search path planning problem involving a team of homogeneous agents. Applied to a search and rescue setting, the approach takes advantage of objective function separability to efficiently maximize probability of success. A network representation is exploited to simplify modeling, reduce constraint specification and speed-up problem-solving. The proposed ILP approach rapidly yields near-optimal solutions for realistic problems using parallel processing CPLEX technology, while providing for the first time a robust upper bound on solution quality through Lagrangean programming relaxation. Problems with large time horizons may be efficiently solved through multiple fast subproblem optimizations over receding horizons. Computational results clearly show the value of the approach over various problem instances while comparing performance to a myopic heuristic.
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.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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