Author manuscript, published in "VRIC 2013, Laval: France (2013)" The Ghost in the Shell Paradigm for Virtual Agents and Users in Collaborative Virtual Environments for Training
Bibliographic record
Abstract
In Collaborative Virtual Environment for Training (CVET), different roles need to be played by actors, i.e. virtual agents or users. We introduce in this paper a new entity, the Shell, which aims at abstracting an actor from its embodiment in the virtual world. Thus, using this entity, users and virtual agents are able to collaborate in the same manner during the training. In addition to the embodiment’s control, the Shell gathers and carries knowledge and provides interaction inputs. This knowledge and those inputs can be accessed and used homogeneously by both users and virtual agents to help them to perform the procedure. In this paper, we detail the knowledge mechanism of the Shell as this knowledge is a crucial element for both collaboration and learning in the CVET context. Furthermore, we also validate our exposed model by presenting an operational implementation in an existing CVET and discuss of its possible usages. Categories and Subject Descriptors [Software and its engineering]: Software organization and properties- Virtual worlds training simulations; [Humancentered computing]: Human computer interaction- Collaborative interaction
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.561 | 0.228 |
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".