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 are available upon request.

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