Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The entity-relationship model—toward a unified view of data
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
MDA Explained: The Model Driven Architecture: Practice and Promise
MDA Explained: The Model Driven Architecture: Practice and Promise
A Model for Reasoning About Persistence and Causation
A Model for Reasoning About Persistence and Causation
An Intelligent Assistant for Training of Power Plant Operators
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
ATL: A model transformation tool
Science of Computer Programming
Engineering Service Oriented Systems: A Model Driven Approach
Engineering Service Oriented Systems: A Model Driven Approach
Software Patterns in ITS Architectures
International Journal of Artificial Intelligence in Education
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Automated compilation of Object-Oriented Probabilistic Relational Models
International Journal of Approximate Reasoning
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Model driven support for the Service Oriented Architecture modeling language
Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems
Explanation of Bayesian Networks and Influence Diagrams in Elvira
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
PRoModel is a software environment that facilitates and expedites the development of systems that handle uncertainty. Uncertainty arises when information is incomplete or incorrect or if we have deficient models that limit the way in which knowledge can be represented. Probabilistic Relational Models (PRMs) are structures that merge, in the same model: (1) the entities and their relationships from an Entity-Relationship model, and (2) the random variables and conditional dependence associations that represent the uncertainty in the domain. PRoModel use PRMs to generate, automatically, the code artifacts and the database to produce a functional prototype of a Web application. In the same way, PRoModel generates the uncertainty model to allow the propagation of evidence and inference of knowledge. The evaluation of PRoModel was made through the development of an Intelligent Tutoring System with a simulator for training operators of power plants. Initial tests show that PRoModel expedited the development of systems that handle uncertainty, providing significant savings in time and effort.