Modeling Reactive Game Agents Using The Cell-Devs Modeling Formalism
Bibliographic record
Abstract
Intelligent game agents are vital to modern games as they add life, story, and immersion to the game environment. The requests in the gaming industry for more realism have made intelligent agents more important than ever. Modeling and simulation of game agents and their surrounding environment provide an alternate setting to study dynamic agent behavior before integration into the game engine. The Cell-DEVS formalism, an extension of Cellular Automata, allows modeling such behaviors using the rigorously formalized Discrete Event Systems Specification (DEVS) formalism. This paper explains how to model and test reactive game agents using the Cell-DEVS formalism and the CD++ toolkit. To analyze the dynamic behavior of such agents, we perform several experiments in varying system configurations. Our experimental results confirm the versatility of Cell-DEVS and the functionalities in the CD++ toolkit to model comfort-driven, exploratory, and desire-driven game agents.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".