Stock Picking

Quantitative investment methods have gained foothold in the financial world in the last ten years. This paper shows how Bayesian Networks can be used to create a computerized stock-picking model.

Learning to Traverse Doors Using Bayesian Networks

By: Elena Lazkano and Basilio Sierra

Mobile robots need to navigate in their environment in order to perform useful tasks. Doors appear in almost every office-like indoor environment and often doors have to be crossed during the navigation process. We believe that visual information may help to anticipate that a door has to be crossed and that the visual information could be combined with proximity sensors in order to select a good position from which the door crossing behavior could start.

HUGIN as a decision making tool in tunnel construction design stage

Currently European regulations on tunnel design and construction are diverse, the variation occurring even within countries and regions or departments. This together with more recent concerns regarding consequences of tunnel fires have resulted in the search of new methods to aid on the decision making at the planning stage in tunnel construction.

Trouble Shooting - Automated Decision Support for Customer Support Operations

It is a complex task to troubleshoot a printing system that consists of several components, like the application the user is printing from, the printer driver, the network connection, the server controlling the printer, the printer itself, and all the subcomponents of these components, etc. The printer industry spends millions of dollars a year on troubleshooting operations. Given observed symptoms, this new breed of troubleshooting systems can compute optimal sequences of troubleshooting steps, and thereby reduce the expected cost of repair to a minimum.