Bibliographic record
Abstract
Games have been used extensively to study human behavior. Researchers in the field of human-robot interaction (HRI) are becoming more aware of the importance of designing compelling and playful games to study interrelationships among players. Despite the growing interest, the use of game design techniques in the creation of playful experiences for HRI experiments is still in its infancy and more multidisciplinary activities should be promoted to foster the convergence between game research and HRI. This workshop aims at discussing the value of using iterative game design techniques to integrate playful experiences using social robots for HRI experiments. More concretely, we want to explore tools, approaches and methods used in previous experiences for appropriate design of interactive games in HRI. Furthermore, based on previous research, a taxonomy for game design using social robots will be presented and attendees will have access to hands-on material created to facilitate the design of interactive games considering important aspects of the robotic systems to maximize the fun experience. We hope this workshop will bring to HRI researchers, game designers, roboticists, and technology enthusiasts enlightening thoughts and ideas to confront the often complicated and time-demanding process of designing compelling games for HRI experiments.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.132 | 0.044 |
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".