It's all in the game: Towards an affect sensitive and context aware game companion
Bibliographic record
Abstract
Robot companions must be able to display social, affective behaviour. As a prerequisite for companionship, the ability to sustain long-term interactions with users requires companions to be endowed with affect recognition abilities. This paper explores application-dependent user states in a naturalistic scenario where an iCat robot plays chess with children. In this scenario, the role of context is investigated for the modelling of user states related both to the task and the social interaction with the robot. Results show that contextual features related to the game and the iCat's behaviour are successful in helping to discriminate among the identified states. In particular, state and evolution of the game and display of facial expressions by the iCat proved to be the most significant: when the user is winning and improving in the game her feeling is more likely to be positive and when the iCat displays a facial expression during the game the user's level of engagement with the iCat is higher. These findings will provide the foundation for a rigorous design of an affect recognition system for a game companion.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".