Intelligent agents, simulation, and gaming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial intelligence and intelligent agents are sources of synergy for simulation and computer-based games. They support striking realism of the physical environment and provide unique opportunities for learning and complex operations. This article's purpose is to explore the relationship of software agents to simulation and games. This includes agents with advanced cognitive abilities (introspection, perception, anticipation, and understanding) as well as those representing personality, emotion, and cultural aspects of individuals and societies including issues. A recent special issue of Simulation: Transactions of the Society for Modeling and Simulation International on agent-directed simulation (ADS) is introduced. As a prelude to its presentation, the promising synergy of artificial intelligence, simulation, and gaming is elaborated on. A unifying paradigm for the synergy of agents and simulation and gaming—namely, ADS—is presented. It includes agent simulation, agent-supported simulation, and agent-based simulation. Also, two different usages of the term agent-based simulation are clarified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it