Probabilistic Similarity Networks

From Tetherless World Wiki

Jump to: navigation, search

Citation: David Heckerman. (1990) Probabilistic Similarity Networks. In KSL-90-30, June,1990.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author David Heckerman
title Probabilistic Similarity Networks
number KSL-90-30
institution Knowledge Systems, AI Laboratory
year 1990
month June
Bibtex more
Access Paper
abstract We address the pragmatics of constructing normative expert systems, and examine the influence diagram as a potential framework for representing knowledge in such systems. We introduce an extension of the influence-diagram representation called a similarity network. A similarity network is a tool for constructing large and complex influence diagrams. The representation allows a user to construct independent influence diagrams for subsets of a given domain. A valid influence diagram for the entire domain can then be constructed from the individual diagrams. Similarity networks represent asymmetric forms of conditional independence that are not represented conveniently in an ordinary influence diagram. We discuss in detail one such conditional independence, called subset independence, and examine how similarity networks exploit this form of independence to facilitate the construction of an influence diagram. Also, we investigate the assessment of probability distributions for influence diagrams. We see that similarity networks exploit subset independence to simplify such probability assessments. We introduce a representation that is closely related to similarity networks, called a partition. This representation further exploits subset independence to simplify probability assessment. Finally, we examine a real-world normative expert system for the diagnosis of lymph-node pathology, called PATHFINDER. The similarity-network and partition representations played a crucial role in the construction of this expert system.

KSL Technical Report ID: KSL-90-30
Facts about Probabilistic Similarity NetworksRDF feed
Abstract We address the pragmatics of constructing … We address the pragmatics of constructing normative expert systems, and examine the influence diagram as a potential framework for representing knowledge in such systems. We introduce an extension of the influence-diagram representation called a similarity network. A similarity network is a tool for constructing large and complex influence diagrams. The representation allows a user to construct independent influence diagrams for subsets of a given domain. A valid influence diagram for the entire domain can then be constructed from the individual diagrams. Similarity networks represent asymmetric forms of conditional independence that are not represented conveniently in an ordinary influence diagram. We discuss in detail one such conditional independence, called subset independence, and examine how similarity networks exploit this form of independence to facilitate the construction of an influence diagram. Also, we investigate the assessment of probability distributions for influence diagrams. We see that similarity networks exploit subset independence to simplify such probability assessments. We introduce a representation that is closely related to similarity networks, called a partition. This representation further exploits subset independence to simplify probability assessment. Finally, we examine a real-world normative expert system for the diagnosis of lymph-node pathology, called PATHFINDER. The similarity-network and partition representations played a crucial role in the construction of this expert system. in the construction of this expert system.
Author David Heckerman  +
Bibtype techreport  +
Has author David Heckerman  +
Has identifier KSL-90-30  +
Has publishing details June,1990  +
Has title Probabilistic Similarity Networks  +
Has where published KSL-90-30  +
Has year 1990  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-90-30  +
Month June  +
Number KSL-90-30  +
Process note NO  +
Title Probabilistic Similarity Networks  +
Year 1990  +
Retrieved from "http://tw.rpi.edu/wiki/Probabilistic_Similarity_Networks"
Personal tools