To Commit or Not to Commit: Modeling Agent Conversations for Action
Bibliographic record
Abstract
Conversations are sequences of messages exchanged among interacting agents. For conversations to be meaningful, agents ought to follow commonly known specifications limiting the types of messages that can be exchanged at any point in the conversation. These specifications are usually implemented using conversation policies (which are rules of inference) or conversation protocols (which are predefined conversation templates). In this article we present a semantic model for specifying conversations using conversation policies. This model is based on the principles that the negotiation and uptake of shared social commitments entail the adoption of obligations to action, which indicate the actions that agents have agreed to perform. In the same way, obligations are retracted based on the negotiation to discharge their corresponding shared social commitments. Based on these principles, conversations are specified as interaction specifications that model the ideal sequencing of agent participations negotiating the execution of actions in a joint activity. These specifications not only specify the adoption and discharge of shared commitments and obligations during an activity, but also indicate the commitments and obligations that are required (as preconditions) or that outlive a joint activity (as postconditions). We model the Contract Net Protocol as an example of the specification of conversations in a joint activity.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".