Characterization of user behavior in a multi-player online game
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
In recent years, multi-player online games (MOGs) have gained enormous popularity and become a major trend in the entertainment industry. Much research has been focusing on improving the performance and scalability of MOG systems. However, relatively little attention has been paid to the study of user behavior. As with other complex interactive applications, a good understanding of user behavior is important to the design of MOG systems. In this paper, we discuss the advantages of a user behavior workload model to the software industry and the research community, and describe a method to develop such a model. For illustrative purposes, we study user behavior by using the measurement data collected from an existing MOG system. This includes the characterization of the interarrival time of logon users, the transition probability of avatars within the virtual environment, the residence time at a room, and the session length. Our results not only provide an insight into user behavior in MOGs, but they are also useful in the development of workload models in performance studies of MOG systems.
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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 | 0.001 |
| 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 it