Implementation of Finite State Machine Models on the Artificial Intelligence System of Characters in The Game "MMORPG" using RPG Maker
Bibliographic record
Abstract
Technological developments are the main drivers of global social, economic, and cultural change, including in the rapidly growing gaming industry. The Role-Playing Game (RPG) genre, in which players portray characters in the game's story, is gaining popularity. The application of FSM Models and AI technology in character development and RPG game interaction not only resulted in exciting entertainment, but also inspired similar uses in various fields. With AI, characters interact dynamically with players and environments, and FSM Models govern complex character behavior, the game experience is even more immersive. RPG Maker, one of the popular engines, simplifies the process of creating RPG games with an easy user interface. The implementation of the FSM Model is done through events and switches, directing storylines and character situations with structured logic. This study analyzes the application of FSM Model in MMORPG RPG games. Through the design, testing, and analysis stages, FSM proved effective in creating games that combine entertainment with learning. This game invites players to look for requirements and challenges to proceed to the next level. The result is an MMORPG game played on a PC with a Windows operating system, providing an educational and entertaining gaming experience.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".