"From Saying to Doing" - Natural Language Interaction with Artificial Agents and Robots
Bibliographic record
Abstract
In this paper we presented a framework for action descriptions and its connection to natural language interfaces for artificial agents. The core point of this approach is the use of a generic action/object hierarchy, which allows interpretation of natural language command sentences, issued by a human user, as well as planning and reasoning processes on the conceptual level, and connects to the level of agent executable actions. The linguistic analysis is guided by a case frame representation, which provides a connection to actions (and objects) represented on the conceptual level. Planning processes can be implemented using typical precondition and effect descriptions of actions in the conceptual hierarchy. The level of primitive actions (leaf nodes of this conceptual hierarchy) connects to the agents' executable actions. The level of primitive actions can thus be adapted to different types of physical agents with varying action sets. Further work includes the construction of a suitable, general action ontology, based on standard ontologies like FrameNet (ICSI, 2007), Ontolingua (KSL, 2007), Mikrokosmos (CRL, 1996), Cyc (Cycorp, 2007), or SUMO (Pease, 2007; IEEE SUO WG, 2003), which will be enriched with precondition and effect formulas. Other topics to be pursued relate to communication of mobile physical agents (humans) in a "speech-controlled" environment. The scenario is related to the "smart house" but instead of being adaptive and intelligent, the house (or environment) is supposed to respond to verbal instructions and questions by the human user. A special issue, we want to address, is the development of a sophisticated context model, and the use of contextual information to resolve ambiguities in the verbal input and to detect impossible or unreasonable actions.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".