From Single to Multiplayer Mobile Bluetooth Gaming
Bibliographic record
Abstract
The growth of mobile phone sales is phenomenal, with estimated sales for 2007/2008 expected to be approximately $1 billion. The majority of these devices are Java-enabled, giving rise to a huge market within the realm of computer games. Most of today’s mobile games are designed to execute on as many phones as possible. Thus, they focus on MIDP 1.0 technology; such devices have very limited resources compared to the top-of-the-line phones of today. The primary reason for this is to maximize profits by having the game reach as wide a potential market as possible. Mobile technology is ever advancing, and the capabilities of the lower-end devices will continue to improve. Perhaps in the not-too-distant future, we will see all of the lower-end mobiles Bluetooth-enabled and supporting more advanced Java implementations. This chapter examines the world of mobile gaming. In particular, it looks at what is needed to produce a single-player game and what elements are necessary to modify it to allow for multiplayer gaming over a Bluetooth network. A framework is presented to allow for the rapid transformation of a single-player to multiplayer game, along with a game engine that can be used for the development of the graphical elements, such as the background and sprites. The multiplayer framework makes use of the Mobile Message Passing Interface (MMPI) to simplify the creation of the network connections and interdevice communication.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".