A Probabilistic Logic to Reason about the Interaction between Knowledge and Social Commitments in MASs
Bibliographic record
Abstract
In this paper, we develop a probabilistic approach to formally represent and reason about the interaction between knowledge and social commitments in Multi-Agent Systems (MASs). Unlike existent approaches that address social commitments within the scope of agent to agent, our approach considers commitments among multiple agents as well. In particular, we introduce a new logic called the probabilistic logic of knowledge and commitment (PCTLkc+). The logic we introduce combines a logic of knowledge and commitment (CTLKC+) with a probabilistic branching-time logic (PCTL). We then extend the resulting logic by adding further operators for the group knowledge and group commitment so that different flavors of the interaction between the two concepts can be nicely captured and reasoned about. A major contribution of this paper is the semantics of group social commitment, which has not been considered in the literature. Target systems are modeled using a new extended version of interpreted systems, a popular formal framework that models the communication between interacting agents and accounts for the uncertainty in MASs. The advancement of the proposed logic over existing logics lies in its expressiveness power that allows one to not only express knowledge and social commitments independently, but also express combinations between them in the presence of uncertainty when the scope of interacting agents goes beyond two.
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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 | 0.001 | 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 | 0.001 | 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".