Knowledge Engineering for Bayesian Networks Ann Nicholson School

Knowledge Engineering for Bayesian Networks Ann Nicholson School of Computer Science and Software Engineering Monash University

Overview l l Representing uncertainty Introduction to Bayesian Networks » Syntax, semantics, examples l l The knowledge engineering process Open research questions

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

Probability theory for representing uncertainty l 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) l Posterior probability (conditional) » P(“it wil rain today” | “rain is forecast”) = 0. 8 l Bayes’ Rule: P(H|E) = P(E|H) x P(H) P(E)

Bayesian networks l l l 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 l 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 TYe 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 l l Evidence: observation of specific state Task: compute the posterior probabilities for query node(s) given evidence. Flu Y Te TYe Th Th Diagnostic inference Causal inference Flu TB Te Flu Te Th Intercausal inference

BN software l Several commerical packages » Netica, Hugin, Analytica (all with demo versions) » Free software: Smile, Genie, Java. Bayes, … » [Add Almond and Murphy BN info sites] » http: //HTTP. CS. Berkeley. EDU/~murphyk/Bayes/bnsoft. html l Examples

Decision networks l Extension to basic BN for decision making » Decision nodes » Utility nodes l EU(Action) = p(o|Action, E) U(o) o » choose action with highest expect utility l Example

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

Knowledge Engineering Process l l 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 l There is much interest in automated methods for learning BNS from data » parameters, structure (causal discovery) l 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 l 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 l l l 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 Ca. MML 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 ·Select next item System Controller Module Sequencing tactics 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 l l l 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 l Tools needed to support expert elicitation » reduce reliance on BN expert » example - visualisation of explanatory methods l Combining expert elicitation and automated methods » Evaluation measures and methods l Industry adoption of BN technology

Visit to Uni. Melb l l 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.