Collaborative behavior-based approach for robot natural language interfaces
Bibliographic record
Abstract
This thesis describes a novel approach called the collaborative behavior-based approach. The approach is used to create an intelligent robot natural language interface so that ambiguous and uncertain human user instructions can be transformed into robot behavior-based control commands. Special features of a user-robot system have been taken into account. Knowledge about the robot world, predicted and history behaviors of the robot and the user are used to resolve ambiguity and uncertainty in interpreting the user instructions. The knowledge is stored in three knowledge bases namely world, history, and behavior. The world knowledge base stores information about the robot word's objects, relationships between the objects, and possible behaviors of the robot and the user. The behavior knowledge base stores information about a sequence of predicted behaviors of the robot and the user in completing services. The history knowledge base stores behaviors of the robot that already occur in completing a service. The fuzzy or possibility theory is applied to the knowledge and used to choose the most plausible meaning of the instructions. The approach has been implemented and experimented. Four of the test cases relating to housekeeping services have been presented in this thesis. The results suggest that the collaborative behavior-based approach is successful.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".