Multiobjective Neuromodulated Controllers for Efficient Autonomous Vehicles with Mass and Drag in the Pursuit-Evasion Game
Why this work is in the frame
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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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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