Knowledge Engineering for Bayesian Networks

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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
• Open research questions

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Sources of Uncertainty

• Ignorance
• Inexact observations
• Non-determinism
• AI representations
• Probability theory
• Dempster-Shafer
• Fuzzy logic

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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)

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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)

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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
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BN inference

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

Flu
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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

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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

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Elicitation from experts

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

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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.

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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

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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

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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.

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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

Item type
System Controller Module
Item
Decimaliens
New game
Sequencing tactics
Number between
Help
Help
.
Report on student
Classroom Teaching Activities
Teacher
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Case Study Bayesian poker
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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

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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

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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.