The Semantic Interpretation of Trust in Multiagent Interactions
Authors: Anup Kalia
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated our approach via an empirical study with 30 subjects (computer science students). |
| Researcher Affiliation | Academia | Anup K. Kalia Department of Computer Science North Carolina State University Raleigh, NC 27695-8206, USA akkalia@ncsu.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code related to the methodology described. |
| Open Datasets | Yes | We asked the subjects to read 33 emails selected from the Enron email corpus (Fiore and Heer 2004) |
| Dataset Splits | Yes | We divided the data collected from subjects into three-fold training and test data and learned trust parameters for each subject (rin, sin, ir, is, λ) that minimize the mean absolute error (MAE) between predicted and actual trust values (Kalia, Zhang, and Singh 2013). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory, or cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a 'chat interface' and 'Bayesian models' trained with 'Expectation Maximization' but does not specify any software names with version numbers. |
| Experiment Setup | Yes | We collected the trust values (actual) from the subjects from the emails assigned to them. We divided the data collected from subjects into three-fold training and test data and learned trust parameters for each subject (rin, sin, ir, is, λ) that minimize the mean absolute error (MAE) between predicted and actual trust values (Kalia, Zhang, and Singh 2013). We used the data collected to train different Bayesian models constructed based on our assumptions using Expectation Maximization. |