Evolution of Multiobjective Neuromodulated Neurocontrollers for Multi-Robot Systems
Bibliographic record
Abstract
This thesis focuses on advancing our understanding of the evolution of multiobjective neurocontrollers that have the ability to perform unsupervised learning while operating.We begin with some biologically-inspired modifications to a standard neuroevolution algorithm, that add unsupervised learning and inheritance of said learning for the benefit of offspring generations.We then explore and analyze the relationship between neurocontroller topology and function.Next, a series of modifications to improve the performance of the evolutionary algorithm, and to adapt it for coevolution are presented, along with the results of a series of experiments used to demonstrate their effectiveness.Finally, we present an experiment designed to determine the ability of the complete method to cross the boundary between simulation and reality.Synaptic plasticity has been shown to facilitate unsupervised learning by adapting neural network weights.Neuromodulation is a biologically-inspired technique that can adapt the per-connection learning rates of synaptic plasticity.Multiobjective evolution of neural network topology and weights has been used to design neurocontrollers for autonomous robots.Lamarckian inheritance has been demonstrated with neuroevolution to pass on learned behaviour from parent to offspring generations.Two previous investigations are presented here.Firstly, multiobjective evolution of network weights and topologies (NEAT-MODS) is augmented with neuromodulated iii
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".