  Normative expert systems have not become commonplace because they have been
difficult to build and use. Over the past decade, however, researchers have
developed the influence diagram, a graphical representation of a decision
maker's beliefs, alternatives, and preferences that serves as the knowledge
base of a normative expert system. Most people who have seen the representation
find it intuitive and easy to use. Consequently, the influence diagram has
overcome significantly the barriers to constructing normative expert systems.
Nevertheless, building influence diagrams is not practical for extremely large
and complex domains. In this book, I address the difficulties associated with
the construction of the probabilistic portion of an influence diagram, called a
knowledge map, belief network, or Bayesian network. I introduce two
representations that facilitate the generation of large knowledge maps. In
particular, I introduce the similarity network, a tool for building the network
structure of a knowledge map, and the partition, a tool for assessing the
probabilities associated with a knowledge map. I then use these representations
to build Pathfinder, a large normative expert system for the diagnosis of
lymph-node diseases (the domain contains over 60 diseases and over 100 disease
findings). In an early version of the system, I encoded the knowledge of the
expert using an erroneous assumption that all disease findings were
independent, given each disease. When the expert and I attempted to build a
more accurate knowledge map for the domain that would capture the dependencies
among the disease findings, we failed. Using a similarity network, however, we
built the knowledge-map structure for the entire domain in approximately 40
hours. Furthermore, the partition representation reduced the number of
probability assessments required by the expert from 75,000 to 14,000.
