Bibliographic record
Abstract
This thesis investigates how the evader in a pursuit-evasion differential game can learn its control strategies using the fuzzy actor-critic learning algorithm.The evader learns its control strategies while being chased by a pursuer that is also learning its control strategy in two pursuit-evasion games; the homicidal chauffeur game and the game of two cars.The simulation results presented in this thesis prove that the evader is able to learn its control strategy effectively using only triangular membership functions and only updating its output parameters.When compared with the simulation results from [1], the approach in this thesis saves a significant amount of computation time.This thesis also introduces fuzzy-actor critic learning to an inertial missile guidance problem.The missile solely relies on its own control surfaces to intercept the target.Proportional navigation is one of the techniques in literature that can successfully guide the missile to the target.The simulation results presented in this thesis suggests that fuzzy actor-critic learning can successfully guide a missile with simple dynamics to the target.iii T (.) Transition function P Probability function
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".