Reasoning about social commitments in the presence of uncertainty
Bibliographic record
Abstract
Interaction among autonomous agents in Multi-Agent Systems (MASs) is a key aspect for agents to coordinate with one another. Social approaches, as opposite to the mentalistic approaches, have received a considerable attention in the area of agent communication recently. They exploit observable social commitments to develop a verifiable formal semantics by which communication protocols can be specified. However, treating social commitments in stochastic systems is entirely missing in the literature. In this paper, we present a new logical language called Probabilistic Computation Tree Logic of Commitments, PCTLC for short, to specify and reason about social commitments in systems exhibiting uncertainty. The proposed modal logic extends PCTL with modalities for commitments and their fulfillments. We model MASs using two extended versions of interpreted systems to capture the probabilistic behavior of MASs, and account for the communication between the interacting agents.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".