Agent-based Crowd Simulation Modeling in a Gaming Environment
Bibliographic record
Abstract
Human crowds are often studied by experts from various areas for planning, training, investigation, etc. Furthermore, simulating the motion of crowd for realistic animation is a popular subject in the computer graphics and video games communities. Thanks to improvements in computing power, modeling and simulations, using computers, are now used to investigate the dynamics of crowds using agents. This paper proposes a novel agent-based crowd simulation model, using a game engine. Compared to the existing techniques, our proposed model is more flexible and, has the ability to make real-time changes in crowd-modeling. The agent generation method, known as Distinguishable Agents Generating Method (DAGM), are capable of creating 3D animated-humanoid models with distinguished attributes. In this paper, we also propose a Multiple layer Collision System (MCS) that includes a system to collect collision messages and to evaluate the processing. A Building & City-planning Generating System (BCGS) is also introduced to create and setup obstacles in the simulation. This approach can be used for studies, planning and training related to crisis management such as stampede, evacuation, etc. The proposed method is capable of rendering a visually vivid 3D crowd of hundreds of agents using a typical computing environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".