Intelligent Systems group, Department of Information and Computing Sciences, Utrecht University (the Netherlands)
Sense making in intelligence analysis often involves stories or scenarios (e.g. the alternative hypotheses in ACH) and arguments (e.g. the pro and con reasons in IBIS). Many of the existing sense making techniques treat stories and arguments in a structured but informal way, that is, they do not provide any formal mathematical semantics. This lack of formal underpinning means that it is not possible to use powerful techniques – such as sensitivity analysis and process verification – to improve the intelligence analyses. However, care must be taken when introducing more complex mathematical models, as they can seriously impede the sense making process.
In this talk, I discuss a hybrid theory of stories and arguments, and specifically how this theory provides a logical or probabilistic semantics for stories and arguments. I show how ideas from Artificial Intelligence can be used to improve and (partly) automate the sense making process, whilst at the same time sticking close to natural and familiar concepts in existing sense making techniques. I illustrate the theory with a case study from the Dutch National Police, with whom we are working together to improve the intake and investigation processes surrounding cyber- and e-crime.
Floris Bex is a lecturer at the Intelligent Systems group of the Department of Information and Computing Sciences, Utrecht University (the Netherlands). He is interested in how people reason, how this reasoning can be captured in formal models and how it can be supported and improved using smart technologies. A core aim of his is to develop tools that can be used to disseminate and analyse complex reasoning involving large amounts of data, such as legal & forensic reasoning, reasoning in the design of complex systems and opinions on the Web.
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