A two stage learning technique using PSO-based FLC and QFIS for the pursuit evasion differential game
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.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".