An approach to comprehensive trust management in multi-agent systems with credibility
Bibliographic record
Abstract
Security is a substantial concept in multi-agent systems where agents dynamically enter and leave the system. Different models of trust have been proposed to assist agents in deciding whether to interact with requesters who are not known (or not very well known) by the service provider. To this end, in this paper we progress our work on security for agent-based systems, which is embedded in service provider’s trust evaluation of the counter part. Agents are autonomous software equipped with advanced communication (using public dialogue game-based protocols and private strategies on how to use these protocols) and reasoning capabilities. The service provider agent obtains reports provided by trustworthy agents (regarding to direct interaction histories) and referee agents (in the form of recommendations) and combines a number of measurements, such as number of interactions and timely relevance, to provide an overall estimation of a particular agent’s likely behavior. Requesting this agent, called the target agent, to provide the number of interactions it had with each agent, the service provider penalizes the agents who lied about having information for trust evaluation process. In addition, after a periodic time, the actual behavior of the target agent is compared against the information provided by others. This comparison leads to both adjusting the credibility of the contributing agents in trust evaluation and improving the system trust evaluation by minimizing the estimation error. Overall the proposed framework is shown to assist agents effectively perform the trust estimation of interacting agents.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".