Game network traffic simulation by a custom bot
Bibliographic record
Abstract
Minecraft is a popular video game played worldwide, and is built simply enough to be used for network analysis and research. This paper describes an automated software agent created to simulate player traffic within the game. Realistic network traffic simulation was the goal that inspired the creation of our “Minecraft bot”: an automatic program or bot that could act in similar ways to a real player, and be able to be mass produced to saturate a local area network. This will facilitate network research by allowing users to have a more scalable testing environment and thus enable controlled laboratory experiments that are impossible to set up in live online gaming environments. The basic commands in Minecraft consist of moving, placing and breaking blocks (pieces of environment) and a realistic bot needs to replicate these actions. Another important objective was to have the ability to create hundreds or thousands of bots doing the same actions, to be able to create artificial latency on the network. This paper will go through the entire lifecycle of our project, starting with some information on existing research about the subject, and how it relates to ours. Following that we describe our bot requirements, the work that was done to find a pre-built solution, the solution we ended up using and how it was modified to fit our requirements. We then have a section showing performance experiments we ran, which compared the packet count and traffic volume between players and bots, as well as cpu usage statistics as more connections were made to the server to ensure that our server hardware was not a factor in our network testing. The final section is the conclusion which talks about the outcome of our project in relation to our original goals, and how it will impact future research in this area.
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.
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 | 0.000 | 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 | 0.000 |
| 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 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".