MAS Controlled NPCs in 3D Virtual Learning Environment
Bibliographic record
Abstract
Incorporating intelligence and social behaviours into virtual worlds for learning is becoming more desirable in making them smart, adaptive, personalized, and therefore, more effective and engaging. Realistic non-player controlled characters (NPCs) are essential of a game world and are making the virtual world more real for players. This is true in video games where more interactive NPCs support the story narrative of a game, making the game more immersive, more convincing, but it is also true in other areas where virtual worlds are used such as business and education, increasing the effectiveness of those environments. Research that has been done with virtual agents and multi-agent systems can be leveraged to create more realistic NPCs through purposeful communication channels, inter-agent interactions and environment-agent interactions for game-based learning applications. This research proposes an approach to controlling avatars with intelligent agents through the creation of an interface between a multi-agent system to a virtual world engine. Basic NPC behaviours controlled by agents using Jason Agent Speak are used to test the feasibility of the approach. A Quizmaster is designed and illustrated as a proof of concept of the use of such agent controlled avatars in educational context.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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; both teacher heads agree on what is shown here.
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".