LifeBrush: Painting Interactive Agent-Based Simulations
Bibliographic record
Abstract
Building and interacting with 3D agent-based simulations that contain a large number of agents is a significant challenge. What if we want to create an intricate new arrangement of agents, or reconfigure a large number of agents? We present LifeBrush, a cyberworld for interactively painting large and elaborate multi-agent simulations with commodity virtual reality systems that we can then simulate and explore. Our main methodology uses sketch-based discrete element texture synthesis to paint agent arrangements. We define a map to convert agents to elements in this framework when we paint and back to agents when we simulate. Like creating new colors on a paint palette, we create example agent arrangements and configurations in an example palette. We paint new agents into a scene with sketch-based generative brushes. We also use those brushes to reconfigure agents to match examples created in the palette. Then we simulate, pause the simulation and modify the agents with our sketch-based tools. This iteration loop enables new levels of interactivity for the design, simulation, and exploration of agent-based simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".