Intelligent Agents in Mobile Vehicular Ad-Hoc Networks: Leveraging Trust Modeling Based on Direct Experience with Incentives for Honesty
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we introduce a multi-faceted trust model of use for the application of ad-hoc vehicular networks (VANETs) - scenarios where agents representing drivers exchange information with other drivers regarding road and traffic conditions. We argue that there is a need to model trust in various dimensions and that combining these elements effectively can assist agents in making transportation decisions. We then introduce two new elements to our proposed model: i) distinguishing direct and indirect reports that are shared ii) employing a penalty for misleading reports, to promote honesty. We demonstrate how these two elements together serve to increase the value of the trust model, through a series of experiments of simulated traffic. In brief, we present a framework to facilitate the effective sharing of information in VANET environments between agents representing the vehicles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.011 | 0.009 |
| 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