A two stage learning technique using PSO-based FLC and QFIS for the pursuit evasion differential game
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Bibliographic record
Abstract
This paper presents a two stage learning technique that combines a particle swarm optimization (PSO)-based fuzzy logic control (FLC) algorithm with the Q-Learning fuzzy inference system (QFIS) algorithm. The PSO algorithm is used as a global optimizer to autonomously tune the parameters of a fuzzy logic controller. On the other hand, the QFIS algorithm is used as a local optimizer. We simulate mobile robots playing the differential form of the pursuit evasion game. The game is played such that the pursuer should learn its default control strategy on-line by interacting with the evader. We assume that the evader plays a well defined strategy which is to run away along the line of sight. The pursuer's learning process depends on the rewards received from its environment. The proposed technique is compared through simulation with the default control strategy, the PSO-based fuzzy logic control algorithm, and the QFIS algorithm. Simulation results show that the proposed learning technique outperform the PSO-based fuzzy logic control algorithm and the QFIS algorithm with respect to the learning time which represents an important factor in on-line applications.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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