Bibliographic record
Abstract
This paper introduces agent-case embeddings, a general purpose tool for detecting a variety of solutions produced by an evolutionary algorithm. They can also be used to explore the geometry of the space of problems that agents attempt to solve. Agent-case embeddings permit the comparison of solutions evolved with different representations by directly comparing phenotypes. Use of agent-case embeddings requires that multiple instances of the problems solved by the agent be available or contrivable. Three examples of agent-case embeddings are derived for apoptotic cellular automata, agents playing the iterated prisoner's dilemma, and simple virtual robots performing the Tartarus task. The use of agent-case embeddings is shown to permit visualization of the diversity of evolved agents, demonstrates the impact of changing algorithm parameters, and explores the impact of different representations on evolutionary search. The algorithm parameters explored include population sizes, elite fraction, and choice of variation operators. Agent-case embeddings are used to demonstrate that a novel technique called single-parent crossover can localize evolutionary search in a small part of the adaptive landscape in a controlled manner.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".