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Knowledge Engineering for Bayesian Networks

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

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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
Thermometer temp reading
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
    students misconceptions about decimals
  • 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
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

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)
  • Adaptiveness evaluation
  • 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
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