Knowledge Engineering a Bayesian Network for an Ecological Risk Assessment (KEBN-ERA) Owen Woodberry Supervisors: Ann Nicholson Kevin Korb Carmel Pollino
Overview Background –Ecological Risk Assessment (ERA) –Bayesian Networks (BN’s) –Knowledge Engineering (KE) The Project (KEBN-ERA)
Ecological Risk Assessment (ERA) Objective: –To develop and test a generic framework to be used in the assessment of ecological risks associated with Australian irrigation activities This study is specific to the Goulburn Broken catchment The Bayesian Network is being developed under Dr Carmel Pollino of the Water Studies Center (Monash University)
Bayesian Networks (BN’s) A BN is a probabilistic reasoning tool Enter evidence/observations/interventions Query to find the probability of an event
Knowledge Engineering (KE) What we need –A set of variables (nodes) and their states –A graphical structure –Conditional probability tables for each node This is difficult to elicit from experts
Goals of the Knowledge Engineering field To make the task of knowledge engineering easier by: –Formalising the expert elicitation process –Developing automated tools to aid in these tasks –Integrate the knowledge obtained from each of these methods Ensure that the BN is created correctly Maintenance and continued development of the BN
The Project (KEBN-ERA) Objectives: –Analysis of KE process to date –Identify and undertake improvements –Evaluation of the BN –Implement possible support tools
Some improvements already identified Improving the spatial representation of the network Improving the temporal representation of the network Exploring the effects of combining experimental data with elicited values
Evaluation Methods Using Domain experts –Elicitation review –Sensitivity analysis –Case based evaluation –MATILDA Using Automated methods –Predictive accuracy –Expected Value –Kullback-Leibler divergence –Bayesian Information reward
Conclusion The Project (KEBN-ERA) –Analysis of KE process to date –Improvements –Evaluation –Implement possible support tools