A scenario-driven decision support system for serious crime investigation

Consideration of a wide range of plausible crime scenarios during any crime investigation is important to seek convincing evidence and hence to minimize the likelihood of miscarriages of justice. It is equally important for crime investigators to be able to employ effective and efficient evidence-collection strategies that are likely to produce the most conclusive information under limited available resources. An intelligent decision support system that can assist human investigators by automatically constructing plausible scenarios, and reasoning with the likely best investigating actions will clearly be very helpful in addressing these challenging problems. This paper presents a system for creating scenario spaces from given evidence, based on an integrated application of techniques for compositional modelling and Bayesian network-based evidence evaluation. Methods of analysis are also provided by the use of entropy to exploit the synthesized scenario spaces in order to prioritize investigating actions and hypotheses. These theoretical developments are illustrated by realistic examples of serious crime investigation.

A contract-based system for aerospace aftercare

The logistics of the aerospace aftermarket raises a number of very interesting challenges from the perspective of electronic contracting. This is a highly dynamic domain, where contracts are established between airlines and engine manufacturers, as well as between engine manufacturers all the way down the supply lines, providing a particularly illustrative showcase for the technologies developed in the CONTRACT project. In this paper, we describe such a domain, as well as our modelling of it as a multiagent simulator where the CONTRACT framework has been used to monitor for compliance with norms.