knowledge engineering for bayesian networks l.
Download
Skip this Video
Download Presentation
Knowledge Engineering for Bayesian Networks

Loading in 2 Seconds...

play fullscreen
1 / 18

Knowledge Engineering for Bayesian Networks - PowerPoint PPT Presentation


  • 162 Views
  • Uploaded on

Knowledge Engineering for Bayesian Networks. Ann Nicholson. School of Computer Science and Software Engineering Monash University. Overview. Representing uncertainty Introduction to Bayesian Networks Syntax, semantics, examples The knowledge engineering process Open research questions.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Knowledge Engineering for Bayesian Networks' - humphrey


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
knowledge engineering for bayesian networks

Knowledge Engineering for Bayesian Networks

Ann Nicholson

School of Computer Science

and Software Engineering

Monash University

overview
Overview
  • Representing uncertainty
  • Introduction to Bayesian Networks
    • Syntax, semantics, examples
  • The knowledge engineering process
  • Open research questions
sources of uncertainty
Sources of Uncertainty
  • Ignorance
  • Inexact observations
  • Non-determinism
  • AI representations
    • Probability theory
    • Dempster-Shafer
    • Fuzzy logic
probability theory for representing uncertainty
Probability theory for representing uncertainty
  • Assigns a numerical degree of belief between 0 and 1 to facts
    • e.g. “it will rain today” is T/F.
    • P(“it will rain today”) = 0.2 prior probability (unconditional)
  • Posterior probability (conditional)
    • P(“it wil rain today” | “rain is forecast”) = 0.8
  • Bayes’ Rule: P(H|E) = P(E|H) x P(H)

P(E)

bayesian networks
Bayesian networks
  • Directed acyclic graphs
  • Nodes: random variables,
    • R: “it is raining”, discrete values T/F
    • T: temperature, cts or discrete variable
    • C: colour, discrete values {red,blue,green}
  • Arcs indicate dependencies (can have causal interpretation)
bayesian networks6

X

Flu

Y

Te

Q

Th

Bayesian networks
  • Conditional Probability Distribution (CPD)
      • Associated with each variable
      • probability of each state given parent states

“Jane has the flu”

P(Flu=T) = 0.05

Models causal relationship

“Jane has a

high temp”

P(Te=High|Flu=T) = 0.4

P(Te=High|Flu=F) = 0.01

Models possible sensor error

“Thermometer

temp reading”

P(Th=High|Te=H) = 0.95

P(Th=High|Te=L) = 0.1

bn inference

Flu

Flu

TB

Flu

Flu

Y

Te

Te

Te

Y

Te

Th

Th

Th

Diagnostic

inference

Causal

inference

Intercausal

inference

Intercausal

inference

BN inference
  • Evidence: observation of specific state
  • Task: compute the posterior probabilities for query node(s) given evidence.

Flu

bn software
BN software
  • Several commerical packages
    • Netica, Hugin, Analytica (all with demo versions)
    • Free software: Smile, Genie, JavaBayes, …
    • [Add Almond and Murphy BN info sites]
    • http://HTTP.CS.Berkeley.EDU/~murphyk/Bayes/bnsoft.html
  • Examples
decision networks
Decision networks
  • Extension to basic BN for decision making
    • Decision nodes
    • Utility nodes
  • EU(Action) =  p(o|Action,E) U(o)

o

    • choose action with highest expect utility
  • Example
elicitation from experts
Elicitation from experts
  • Variables
    • important variables? values/states?
  • Structure
    • causal relationships?
    • dependencies/independencies?
  • Parameters (probabilities)
    • quantify relationships and interactions?
  • Preferences (utilities)
knowledge engineering process
Knowledge Engineering Process
  • These stages are done iteratively
  • Stops when further expert input is no longer cost effective
  • Process is difficult and time consuming
  • As yet, not well integrated with methods and tools developed by the Intelligent Decision Support community.
knowledge discovery
Knowledge discovery
  • There is much interest in automated methods for learning BNS from data
    • parameters, structure (causal discovery)
  • Computationally complex problem, so current methods have practical limitations
    • e.g. limit number of states, require variable ordering constraints, do not specify all arc directions
  • Evaluation methods
the knowledge engineering process
The knowledge engineering process

1. Building the BN

  • variables, structure, parameters, preferences
  • combination of expert elicitation and knowledge discovery

2. Validation/Evaluation

  • case-based, sensitivity analysis, accuracy testing

3. Field Testing

  • alpha/beta testing, acceptance testing

4. Industrial Use

  • collection of statistics

5. Refinement

  • Updating procedures, regression testing
case study seabreeze prediction
Case Study: Seabreeze prediction
  • 2000 Honours project, joint with Bureau of Meteorology (PAKDD’2001 paper, TR)
  • BN network built based on existing simple expert rule
  • Several years data available for Sydney seabreezes
  • CaMML and Tetrad-II programs used to learn BNs from data
  • Comparative analysis showed automated methods gave improved predictions.
case study intelligent tutoring
Case Study: Intelligent tutoring

Adaptive

Bayesian

Network

Inputs

Student

Generic BN model of student

Decimal comparison

test (optional)

Item

Answers

Answer

  • Diagnose misconception
  • Predict outcomes
  • Identify most useful information

Information about student e.g. age (optional)

Computer Games

Hidden

number

Answer

Classroom

diagnostic test

results (optional)

Feedback

Answer

Flying

photographer

  • Select next item type
  • Decide to present help
  • Decide change to new game
  • Identify when expertise gained

System

Controller

Module

Item type

Item

Decimaliens

New game

Sequencing

tactics

Number between

Help

Help

….

Report

on student

Classroom

Teaching

Activities

Teacher

consulting experiences
Consulting experiences
  • In 1999/2000, Kevin Korb and myself
  • Clients: NAB, North Ltd
  • Process
    • approached by technical person interested in the technology
    • gave workshops on BN technology
    • brainstorming for BN elicitation (iterative)
    • technical person satisfied with preliminary results
    • BN technology not “sold” to managers
open research questions
Open Research Questions
  • Tools needed to support expert elicitation
    • reduce reliance on BN expert
    • example - visualisation of explanatory methods
  • Combining expert elicitation and automated methods
    • Evaluation measures and methods
  • Industry adoption of BN technology
visit to unimelb
Visit to UniMelb
  • March-June (away some of April/May)
  • Work on BN textbook (joint with Kevin Korb)
  • Continue ongoing research projects
  • Talk with DIS academics with any common interests.