Probability-based models for plausible reasoning

TL;DR: This work focuses on a door because it links one place to another, and the linking of different places and sharing of places is one of the substantial qualities of network technology.

TL;DR: The theory of belief networks offers a relatively new approach for dealing with uncertain information in knowledge-based (expert) systems as discussed by the authors, which is mathematically sound, based on techniques from probability theory, and therefore seems attractive to convert existing rule-based expert systems into belief networks.

TL;DR: A study in which rule-based systems to systems based on Bayesian networks are compared, and two expert systems for diagnosis of liver disorders are presented that served as the inspiration and vehicle of the study.

TL;DR: An overview is given, with new results, of mathematical models and algorithms for Probabilistic logic, probabilistic entailment and various extensions, leading to automated generation of theorems in the theory of probabilities.