SOAR: an architecture for general intelligence
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Cognitive systems engineering
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Cognitive Work Analysis: Towards Safe, Productive, and Healthy Computer-Based Work
Cognitive Work Analysis: Towards Safe, Productive, and Healthy Computer-Based Work
Elkan's Reply: The Paradoxical Controversy over Fuzzy Logic
IEEE Expert: Intelligent Systems and Their Applications
Reasoning about Uncertainty
Universal Meta Data Models
Ecological Interface Design
A Junction Tree Propagation Algorithm for Bayesian Networks with Second-Order Uncertainties
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
| Hi-index | 0.00 |
Information, as well as its qualifiers, or meta-information, forms the basis of human decision-making. Human behavior models (HBMs) therefore require the development of representations of both information and meta-information. However, while existing models and modeling approaches may include computational technologies that support meta-information analysis, they generally neglect its role in human reasoning. Herein, we describe the application of Bayesian belief networks to model how humans calculate, aggregate, and reason about meta-information when making decisions.