Hybrid Hierarchical Learning for Adaptive Persuasion in Human-Robot Interaction
Why this work is in the frame
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Bibliographic record
Abstract
Adaptive learning is critical to helping robots personalize their interactions with people, particularly when considering skills needed by socially assistive robots, such as persuasion. In this letter, we propose a novel, hybrid hierarchical learning architecture for use in social human-robot interaction (HRI) to adapt robot persuasive behaviors to both the static (e.g., need for cognition) and dynamic (e.g., affect) considerations of a user. A learning hierarchy is introduced that uses a contextual bandit approach in the top level to optimize for a static cognition bias and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -Learning in the lower level to optimize selection of a robot persuasive strategy to deploy that aligns with a user's affect. We compare the performance of our system with a non-hierarchical learning method in simulated experiments for the task of persuading people to do daily exercises. The results show that our hybrid hierarchical architecture outperforms a non-hierarchical benchmark in learning speed and robustness to both longitudinal user change and noisy observations. Our architecture is the first to: 1) persuasively adapt to different users during social HRI considering both static and dynamic user change, and 2) use user state decomposition in persuasive HRI.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it