FTE: A Fuzzy Logic Based Trust Establishment Model for Intelligent Agents
Bibliographic record
Abstract
Multiagent systems are commonly used for the study and implementation of distributed systems and virtual online societies. Trust has long been recognized as a vital notion in multiagent systems, where agents can be self-centred, miscellaneous, and misleading. Most existing trust models focus on creating algorithms for trusters to model the honesty of trustees in order to make effective decisions about which trustees to interact with. If agents' interactions are based on trust, trustworthy trustees will have a greater impact on interactions' results. This work describes a fuzzy logic based trust establishment model for intelligent agents using indirect feedback (FTE) that goes beyond trust evaluation to outline actions to guide trustees (instead of trusters). The model uses the retention of trusters and a fuzzy logic engine to model trusters' behaviors. The model uses both the retention behavior of the partnering truster and the average retention rate of all trusters in the society to adjusts the utility gain the trustee provides when interacting with each truster. The proposed model does not depend on direct feedback, nor does it rely on current reputation of trustees in the environment. Simulation results indicate that trustees empowered with the proposed model can be selected more by rational trusters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".