ABSTRACT
This chapter surveys the development of graphical models known as Bayesian networks, summarizes their semantical basis, and assesses their properties and applications to reasoning and planning.
Bayesian networks are directed acyclic graphs (DAGs) in which the nodes represent variables of interest (e.g., the temperature of a device, the gender of a patient, a feature of an object, the occurrence of an event) and the links represent causal influences among the variables. The strength of an influence is represented by conditional probabilities that are attached to each cluster of parents-child nodes in the network.
