Interest management for massively multiplayer games
Bibliographic record
Abstract
The popularity of massively multiplayer games has increased in recent years and game providers are facing scalability problems to accomodate growing populations of users. Broadcasting all state changes to every player is not a viable solution to maintain a consistent game state in a massively multiplayer game. To successfully overcome the challenge of scale, massively multiplayer games have to employ sophisticated interest management techniques that only send relevant state changes to each player. In this thesis we develop a space partitioning technique based on triangulation that adapts to the worldâs obstacles. We introduce obstacle-aware interest management algorithms that use the triangular partitioning to determine the relevance of objects based on the occlusion created by obstacles. We compare the performance of both obstacle-aware and state-of-the-art interest management algorithms based on measurements obtained in a real massively multiplayer game using human and computer-generated player actions. We show that obstacle-aware interest management algorithms can reduce the number of update messages between players and that algorithms based on our triangle-based partitioning can scale to a larger number of objects. The experiments also show that measurements obtained with computer-controlled players performing random actions can approximate measurements of games played by real humans, provided that the traces of the random players are designed adequately. As the size of the world and the number of players of massively multiplayer games increases, adaptive interest management techniques such as the ones studied in this thesis will become increasingly important.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".