We have experience with a wide variety
of Bayesian network applications. Past and current projects include:
- Modelling Threats to Species
Victoria's Department of Sustainability and Environment (DSE) uses the Actions for Biodiversity Conservation system for recording information on threatened species across the state. We created a software component (known as ABC-BN) that fully integrates into this database, allowing species managers to explicitly model the impacts of threats on species and the effectiveness of management actions used to mitigate those threats.
ABC-BN provides a comprehensive set of tools for creating models of the factors affecting a species and exploring the predictions of those models. To keep things simple for managers, a template model structure is used that captures the fundamental elements important to every species' situation: namely, management actions, threats and population factors (such as species numbers or range). For any given species, managers are free to include in their model whatever actions, threats and population factors they believe to be important. Managers can then add links to indicate which actions affect which threats, and which threats impact upon which population factors. Parameterisation of the network is performed using natural language questions and prompts, with the ability to switch to tables and numbers for those more comfortable with BN concepts. After the models are reviewed and accepted, stakeholders can experiment with the created models by entering either certain or uncertain evidence (in the form of likelihoods) and produce reports indicating which actions and other factors are most likely to improve the status of threats and, ultimately, the status of the species.
- Aggregating Expert Forecasts
The US government organisation IARPA established the ACE (Aggregative Contingent Estimation) program in order to develop and improve methods for eliciting conditional predictions from experts and also for aggregating those predictions into a single prediction. One of the teams participating in that program is a group called DAGGRE, based at George Mason University. Together with ACERA, we are assisting DAGGRE with the development of their combinatorial prediction market software, now based on an underlying BN back-end, along with their conditional and unconditional elicitation processes. We additionally created information gathering and elicitation software for ACERA's own elicitation and aggregated prediction experiments.
- Victorian Western Grasslands Reserves
The Victorian Government is reserving 15,000 hectares of land to the west and north-west of Werribee as a way of protecting endangered grasslands and offsetting the ecological costs of urban growth to the north and west of Melbourne. The DSE wishes to provide Parks Victoria with an adaptive management process for these grasslands, which will involve modelling, predicting, monitoring and revising the model in repsonse to discrepancies that arise in the review. DSE has chosen Bayesian networks as the modelling technology, and we are building the initial models to be used in this process, as well as providing training.
- Power Pole Maintenance
Western Power manages over 700,000 power poles in Perth and surrounding areas, many of which are nearing the end of their service lives. Western Power has recently embarked on a project aimed at improving the safety of these poles, while minimising the maintenance cost. The company settled on Bayesian networks as the best approach to model the health of the wood poles, given their strong risk management capabilities. We are providing Western Power with modelling advice and performance-focused BN programming solutions to assist in managing the risk of power pole failure.
- Anomaly Detection
We are creating software for the Defence Science and Technology Organisation based on our machine learning software CaMML and earlier research work to detect anomalous behaviour in track data sets of various kinds (such as ships and cars). This project involves mining large time series data sets in order to produce causal models that yield an understanding of normal behaviour, against which anomalies can be assessed. The emphasis in the current project will be to allow users of the software to create and test models and to provide detection in an online environment.
We are developing models for New Zealand's Scion research institute to minimise the risk of pests appearing in wood chips due for export. The aim is to sufficiently reduce the risk of attracting pests throughout the cutting, transportation and storage stages, such that costly and environmentally-unfriendly fumigation can be avoided prior to export.
Projects the principals have
completed successfully (prior to the establishment of
Bayesian Intelligence in 2008) include:
- Ecological Modelling
We have done a number of ecological modelling projects, including
- providing intelligent decision support for the assessment of
ecological risk associated with irrigation systems in the
intelligent decision support for tropical seagrass in the Great Barrier Reef;
- assessing water quality in Sydney Harbour.
- Bureau of Meteorology
We've done two projects with the Bureau. A prototype project in
2000 used the substantial amount of data gathered in the lead-up to
the Sydney Olympics to model seabreezes. The study used a combination
of expert elicitation and CaMML (Causal Discovery via MML) to find
Bayesian networks that improved upon existing BOM prediction systems.
A second project has developed an ontology for weather forecasting
and a tool for examining and manipulating the ontology; the tool
provides a form of object-oriented Bayesian network design, by
allowing default subnetworks to be associated with weather objects,
which can be extracted, composed and refined. This second project
also involves the development of Bayesian networks for decision support in
forecasting fog and hailstorms.
- Intelligent Tutoring Systems
We have used Bayesian networks to develop an intelligent tutoring system,
DecSys, for children learning decimal
arithmetic, using simple computer game environments. The BN models the
children's understanding of arithmetic and especially their
misconceptions, allowing sensible automated judgements of what
material to present or re-present next. (See DecSys
for more information.)
This project was undertaken in collaboration with researchers at the
University of Melbourne.
- Bayesian Poker
Our Bayesian poker player
(BPP) uses Bayesian networks to model
opponents and to model hands in Texas Hold'em Poker. BPP was
entered into the inaugural and subsequent world automated poker playing
competitions at the American Artificial Intelligence Conference
(AAAI), from 2006. A simple
GUI interface allows people to play
against BPP via the internet.
- Cardiovascular Risk Assessment
Using both expert knowledge and longitudinal data we developed a
Bayesian network model of coronary heart disease, TakeHeart II, which
supports risk assessment for individuals with and without treatment
As a part of this project we developed a scripting language to support
development of GUI interfaces sitting above a Bayesian network which
serves as the inference engine. This allowed us to place a superior
human-computer interface on top of a model that performs as well as
the best published models. This modularity will also allow a simple
should better Bayesian network models become available.
- Biomedical Engineering
We developed dynamic Bayesian networks for ambulation monitoring of
the elderly and to diagnosis falls. The
monitoring is performed using two kinds of sensors: foot-switches,
which report steps, and a mercury sensor, which is triggered by a
change in height. The networks issue an alert when a fall is
- Software Architecture Design
We have used Bayesian networks for change impact analysis in software
Research into system design rationale in the past has focused on the
representation of design deliberations and has omitted the
connections between design rationales and design artefacts. Without
such connections, designers and architects cannot easily assess how
changing requirements or design decisions may affect the system. In
this project, we introduced the Architecture Rationale and Element
Linkage (AREL) model to represent the causal relationships between
architecture elements and decisions. AREL was in turn modelled as a
Bayesian Network, capturing the probabilistic dependency relationships
between the architecture elements and decisions in such an
architecture design model. We demonstrated that such probabilistic
modelling enables architects to quantitatively analyse (i.e. predict
and diagnose) the impact of change in either the requirements or the
This project was undertaken in collaboration with researchers at
References are available upon request.