# Knowledge Engineering for Bayesian Networks - PowerPoint PPT Presentation

PPT – Knowledge Engineering for Bayesian Networks PowerPoint presentation | free to download - id: 73389d-ODJkO

The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
Title:

## Knowledge Engineering for Bayesian Networks

Description:

### Ann Nicholson School of Computer Science and Software Engineering Monash University – PowerPoint PPT presentation

Number of Views:105
Avg rating:3.0/5.0
Slides: 25
Provided by: DavidA276
Category:
Tags:
Transcript and Presenter's Notes

Title: Knowledge Engineering for Bayesian Networks

1
Knowledge Engineering for Bayesian Networks
• Ann Nicholson

School of Computer Science and Software
Engineering Monash University
2
Overview
• Representing uncertainty
• Introduction to Bayesian Networks
• Syntax, semantics, examples
• The knowledge engineering process
• Case Studies
• Seabreeze prediction
• Intelligent Tutoring
• Open research questions

3
Sources of Uncertainty
• Ignorance
• Inexact observations
• Non-determinism
• AI representations
• Probability theory
• Dempster-Shafer
• Fuzzy logic

4
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(HE) P(EH) x P(H)

• P(E)

5
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)

6
Bayesian networks
• Conditional Probability Distribution (CPD)
• Associated with each variable
• probability of each state given parent states

Jane has the flu
P(FluT) 0.05
Models causal relationship
Jane has a high temp
P(TeHighFluT) 0.4 P(TeHighFluF) 0.01
Models possible sensor error
P(ThHighTeH) 0.95 P(ThHighTeL) 0.1
7
BN inference
• Evidence observation of specific state
• Task compute the posterior probabilities for
query node(s) given evidence.

Flu
8
BN software
• Commerical packages Netica, Hugin, Analytica
(all with demo versions)
• Free software Smile, Genie, JavaBayes,
• http//HTTP.CS.Berkeley.EDU/murphyk/Bayes/bnsoft.
html
• Examples

9
Decision networks
• Extension to basic BN for decision making
• Decision nodes
• Utility nodes
• EU(Action) ? p(oAction,E) U(o)
• o
• choose action with highest expect utility
• Example

10
Elicitation from experts
• Variables
• important variables? values/states?
• Structure
• causal relationships?
• dependencies/independencies?
• Parameters (probabilities)
• quantify relationships and interactions?
• Preferences (utilities)

11
Expert Elicitation Process
• These stages are done iteratively
• Stops when further expert input is no longer cost
effective
• Process is difficult and time consuming.
• Current BN tools
• inference engine
• GUI
• Next generation of BN tools?

12
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

13
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

14
Case Study Intelligent tutoring
• Tutoring domain primary and secondary school
• Based on Decimal Comparison Test (DCT)
• student asked to choose the larger of pairs of
decimals
• different types of pairs reveal different
misconceptions
• ITS System involves computer games involving
decimals
• This research also looks at a combination of
expert elicitation and automated methods

15
Expert classification of Decimal Comparison Test
(DCT) results
16
The ITS architecture
Inputs
Student
Generic BN model of student
Decimal comparison test (optional)
Item
• Diagnose misconception
• Predict outcomes
• Identify most useful information

Information about student e.g. age (optional)
Computer Games
Hidden number
Classroom diagnostic test results (optional)
Feedback
Flying photographer
• Select next item type
• Decide to present help
• Decide change to new game
• Identify when expertise gained

Item type
System Controller Module
Item
Decimaliens
New game
Sequencing tactics
Number between
Help
Help
.
Report on student
Classroom Teaching Activities
Teacher
17
Expert Elicitation
• Variables
• two classification nodes fine and coarse (mut.
ex.)
• item types (i) H/M/L (ii) 0-N
• Structure
• arcs from classification to item type
• item types independent given classification
• Parameters
• careless mistake (3 different values)
• expert ignorance - in table (uniform
distribution)

18
Expert Elicited BN
19
Evaluation process
• Case-based evaluation
• experts checked individual cases
• sometimes, if prior was low, true
classification did not have highest posterior
(but usually had biggest change in ratio)
• priors changes after each set of evidence
• Comparison evaluation
• Differences in classification between BN and
expert rule
• Differences in predictions between different BNs

20
Comparison evaluation
• Development of measure same classification,
desirable and undesirable re-classification
• Use item type predictions
• Investigation of effect of item type granularity
and probability of careless mistake

21
Investigation by Automated methods
• Classification (using SNOB program, based on MML)
• Parameters
• Structure (using CaMML)

22
Results
23
Case Study Seabreeze prediction
• 2000 Honours project, joint with Bureau of
Meteorology (PAKDD2001 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.

24
Open Research Questions
• Tools needed to support expert elicitation
• Combining expert elicitation and automated
methods
• Evaluation measures and methods
• Industry adoption of BN technology