Transcript Knowledge Acquisition for Goal Prediction in a Multi

Knowledge Engineering for
Bayesian Networks
Ann Nicholson
School of Computer Science
and Software Engineering
Monash University
Overview
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Representing uncertainty
Introduction to Bayesian Networks
» Syntax, semantics, examples
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The knowledge engineering process
Open research questions
Sources of Uncertainty
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Ignorance
Inexact observations
Non-determinism
AI representations
» Probability theory
» Dempster-Shafer
» Fuzzy logic
Probability theory for
representing uncertainty
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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)
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Posterior probability (conditional)
» P(“it wil rain today” | “rain is forecast”) = 0.8
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Bayes’ Rule: P(H|E) = P(E|H) x P(H)
P(E)
Bayesian networks
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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 networks
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Conditional Probability Distribution (CPD)
– Associated with each variable
– probability of each state given parent states
“Jane has the flu”
FXlu
P(Flu=T) = 0.05
TY
e
P(Te=High|Flu=T) = 0.4
P(Te=High|Flu=F) = 0.01
Models causal relationship
“Jane has a
high temp”
Models possible sensor error
“Thermometer
temp reading”
TQh
P(Th=High|Te=H) = 0.95
P(Th=High|Te=L) = 0.1
BN inference
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Evidence: observation of specific state
Task: compute the posterior probabilities for query
node(s) given evidence.
Flu
Flu
Y
Te
TY
e
Th
Th
Diagnostic
inference
Causal
inference
Flu
TB
Te
Flu
Te
Th
Intercausal
inference
Intercausal
inference
BN software
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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
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Examples
Decision networks
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Extension to basic BN for decision making
» Decision nodes
» Utility nodes
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EU(Action) =  p(o|Action,E) U(o)
o
» choose action with highest expect utility
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Example
Elicitation from experts
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Variables
» important variables? values/states?
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Structure
» causal relationships?
» dependencies/independencies?
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Parameters (probabilities)
» quantify relationships and interactions?
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Preferences (utilities)
Knowledge Engineering Process
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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
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There is much interest in automated methods
for learning BNS from data
» parameters, structure (causal discovery)
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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
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Evaluation methods
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
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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
Adaptive
Bayesian
Network
Inputs
Student
Generic BN
model of student
Decimal
comparison
test
(optional)
Answers
Diagnose
misconception
Predict outcomes
Identify most
useful information
Information about
student e.g. age
(optional)
Classroom
diagnostic test
results (optional)
Answer
Computer Games
Hidden
number
Answer
Feedback
Answer
System
Controller
Module
Sequencing
tactics
Item
Select next item
type
Decide to present
help
Decide change to
new game
Identify when
expertise gained
Item type
Item
Decimaliens
New
game
Help
Number
between
….
Report
on student
Classroom
Teaching
Activities
Teacher
Flying
photographer
Help
Consulting experiences
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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
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Tools needed to support expert elicitation
» reduce reliance on BN expert
» example - visualisation of explanatory methods
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Combining expert elicitation and automated
methods
» Evaluation measures and methods
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Industry adoption of BN technology
Visit to UniMelb
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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.