A GENERIC FORMAL FRAMEWORK FOR CONSTRUCTING AGENT INTERACTION PROTOCOLS
Why this work is in the frame
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Bibliographic record
Abstract
Agent interaction protocols (AIP) design is one of the principal issues for building multi-agent systems. Indeed, the construction of AIP should integrate theories, methodologies and tools. We propose in this paper a unifying framework that provides a generic agent architecture to be reused as well as a methodology to construct and refine AIP specifications in an incremental way. This framework is based on the highly expressive formal language Lotos and its related technologies, such as finite state machines and temporal logics. Hence, the proposed framework also facilitates formal validation and verification of AIP specifications using rigorous tools. We argue that there are three layers of semantics of Lotos specifications that can improve Lotos expressivity in describing agent interaction. Therefore, this framework can describe almost all aspects of agent interaction and at different abstraction levels. In addition, we demonstrate how to generate an online auction protocol from the generic framework, and how to validate and verify this protocol.
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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 | 0.001 |
| 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 | 0.001 |
| 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 it