Multiobjective Neuromodulated Controllers for Efficient Autonomous Vehicles with Mass and Drag in the Pursuit-Evasion Game
Bibliographic record
Abstract
Autonomous vehicles in the pursuit-evasion game, subject to the effects of mass and drag, are controlled using an evolutionary multiobjective neuromodulated controller with unsupervised learning. Multiobjective evolution of network weights and topologies (NEAT-MODS) is extended with Lamarckian-inherited neuromodulated learning. NEAT-MODS is an NSGA-II augmented multiobjective neurocontroller that uses two conflicting objectives. By evolving pursuit agents optimized with the separate and conflicting objectives of `capturing evaders' and `minimizing energy consumption', efficient neurocontrollers can be evolved. NEAT-MODS uses a selection process that aims to ensure Pareto-optimal genotypic diversity and elitism. Neuromodulation is a biologically-inspired technique that can adapt the per-connection learning rates of synaptic plasticity. Lamarckian inheritance allows behaviours learned during parent generations to be passed on to their offspring. The capability of the design is demonstrated in a series of experiments with a simulated evolved vehicle pursuing a basic evader vehicle. It is shown that compact and efficient neurocontrollers for pursuer agents with nonzero mass and drag, capable of capturing an optimal evader while simultaneously minimizing energy consumption, are evolved.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".