Multi-criteria trust establishment for Internet of Agents in smart grids
Why this work is in the frame
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Bibliographic record
Abstract
The Internet of Agents (IoA) is an emerging field of research that aims to combine the advantages of multi-agent systems and Internet of Things (IoT), by adding autonomy and smartness to, traditionally, dummy things used in IoT. Multi-agent systems can be used to model distributed systems of smart grids, such as smart grid operations, power system control, electricity market, and monitoring and diagnostic. Trust management can be considered a key component for successful interactions between autonomous agents in IoA, especially when agents cannot assure that potential interactions’ partners share the same core beliefs, or make accurate statements regarding their competencies and abilities. When interactions are based on trust, trust establishment mechanisms can be used to direct trustees, instead of trustors, to build a higher level of trust and have a greater impact on the results of interactions. This paper presents a trust establishment model that uses a multi-criteria (multidimensional) approach to help trustees in IoA environment to adjust their behaviors to improve their perceived trustworthiness, to attract more interactions with trustors. It calculates the necessary improvement per criterion when only a single aggregated satisfaction value is provided per interaction, where the model attempts to predicted both the appropriate value per criteria and its importance. The proposed model is evaluated through simulation, and results indicate that trustees empowered with the proposed model have higher levels of trust and better chances to be selected as interaction partners when such selection is based on trust.
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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 | 0.000 |
| 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 it