Combining Direct Trust and Indirect Trust in Multi-Agent Systems
Authors: Elham Parhizkar, Mohammad Hossein Nikravan, Robert C. Holte, Sandra Zilles
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our large-scale experimental study shows that strong methods for computing indirect trust make direct trust redundant in a surprisingly wide variety of scenarios. Further, a new method for the combination of the two trust types is proposed that, in the remaining scenarios, outperforms the ones known from the literature. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Regina, Canada 2Department of Computing Science, University of Alberta, Canada parhizkar@uregina.ca, nikravam@uregina.ca, rholte@ualberta.ca, zilles@cs.uregina.ca |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes a simulation environment where interactions are simulated, but it does not specify or provide access to a publicly available or open dataset. |
| Dataset Splits | No | The paper describes a simulation setup where interactions are dynamically generated and processed, but it does not specify fixed training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions various indirect trust systems (ITEA, TRAVOS, ACT, MET) that were compared, but it does not specify any software libraries, programming languages, or other ancillary software with version numbers used for the experiments. |
| Experiment Setup | Yes | We ran each of ITEA, TRAVOS, ACT, and MET in combination with every method in Section 3. ... Our empirical study uses one truster, 10 trustees, and 10 advisors. ... Every number reported in our tables is an average of 100 runs of the same setting; each run has a preprocessing phase in which (i) the trustworthiness value of each trustee is initially sampled uniformly at random, and (ii) 1000 advisor/trustee pairs (ai, sj) are sampled at random. ... The value of λ is set to 0.1 for all experiments. ... we set n = 6 as the trust network size for MET. |