[loginf] PhD in argumentation for crime and security. Deadline 26 May

Federico Cerutti CeruttiF at cardiff.ac.uk
Tue May 10 18:04:49 CEST 2016


Dear Colleague,

Please feel free to forward this message to anyone who might be
interested.

We invite applications for a fully funded (UK/EU fees + stipend) PhD
studentship on argumentation for crime and security. Please see project
description in the following. 

Deadline for applying is on 26th May 2016: please see details at
http://courses.cardiff.ac.uk/funding/R2770.html .

Informal inquiries can be made to Dr. Cerutti (CeruttiF at cardiff.ac.uk).

Regards,

Federico.

==========================
Argumentation in Crime and Security Domain: Argument Mining and Natural
Language Interfaces to Automated Reasoning


Intelligence analysis refers to “the application of individual and
collective cognitive methods to evaluate, integrate, interpret
information about situations and events to provide warning for potential
threats or identify opportunities.” Analysts need (1) to gather evidence
—i.e. information about situations and events—and (2) to generate
sensible hypotheses—i.e. the result of sense-making activities over
evidence— and (3) to effectively communicate them.

The aim of this PhD project is thus threefold:
      * Support evidence gathering: review and assess existing
        techniques for data mining that can be applied to evidence
        gathering in order to reduce the burden on analysts;
      * Support evidence evaluation: develop techniques to transform the
        results of data mining into sensible arguments in favour of
        hypotheses (argument mining) in order to support analysts in
        their sense-making activities over evidence;
      * Support knowledge sharing: develop natural language generation
        techniques for efficiently sharing data and knowledge across
        individuals taking into considerations policies and privacy
        concerns.

Hypotheses inform strategies for preventing threats or coping with
critical situations. To identify them, analysts must combine several
approaches to assess evidence, establish what is credible, and
understand what additional evidence may be required. This needs to be
done in a timely manner, which poses significant challenges for
individual analysts. This combined reasoning process is resistant to
automation which is the reason why we propose to integrate statistical
machine learning with other artificial intelligence technologies.

Although analysts are not the decision makers, we saw in recent, tragic
events the importance of effective intelligence analysis to inform
decision. Every step towards the aforementioned aims will result in more
efficient crime (including terrorism) prevention and control. Indeed, by
providing intelligence analyst with reliable, efficient methods for
evidence evaluation and knowledge sharing, we can improve the process of
intelligence by reducing human errors due to biases and
miscommunication.


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