Projects
Past Projects
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
Goulburn-Broken catchment;
- providing
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
interventions.
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
diagnosed.
- Software Architecture Design
We have used Bayesian networks for change impact analysis in software
architecture design.
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
design.
This project was undertaken in collaboration with researchers at
Swinburne University.
References
References are available upon request.
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