Bibliographic record
Abstract
The use of tools or artifacts is essential to the human race and has been the subject of recent research in Artificial Intelligence.How individual agents acquire these capabilities and how they evolve can be considered vital steps towards understanding complex group capabilities.In a previous study, we designed and implemented an extended version of a theoretical model for artifact capability that accommodated biological evolution and learning via exploratory methods.Historical knowledge and genetic algorithms were combined with learning techniques to build agents that could learn either individually from observations of their own behaviour or socially by observation from a distance.In this study, we incorporate a collaborative form of cultural learning into the model in an effort to enhance the artifact capability-learning agents.This is accomplished via the design of a cultural evolutionary model that utilizes genetic and cultural algorithms to complement the cognitive abilities of the agents.Learning agents belonging to a social network cooperate with and benefit from each other by sharing individual experiences.Results obtained from the multi-agent simulation implementation confirm the efficiency of social learning over individual learning and demonstrate the benefits of cultural over biological evolution.They also suggest that as artifacts get more complex, social agents learning via cultural influence outperform those learning by observation from a distance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".