Several groups in the Department of Computer Science, University of York, use argumentation structures to document a quality related case. In NSC, the CoSMoS project is developing the infrastructure and processes needed for modelling and simulating complex systems: argumentation structures are being used to record the rationale for simulation and the basis of believing that the simulation is adequate for its purpose. In HISE, argumentation has been used for many years in the presentation and analysis of safety cases. Both groups use the Goal Structuring Notation (GSN) to represent arguments.
Currently, GSN argumentation is supported by either commercial tools, or a flaky Microsoft Visio plug-in. The tools are notation-only, and do not provide any supporting material about the argument. There is at least one metamodel for the argumentation concepts.
Example of GSN argument
CoSMoS needs a summer intern to develop a hardware extension to our robots. The lucky intern will get to work on a real robot and will learn a whole lot about robotics, hardware and engineering along the way. That sort of thing looks great on a CV, plus you get paid! Here’s a photo of one of the robots, we have a total of 18:
It’s a really great opportunity if you didn’t manage to get a summer placement in industry this year. I wish I could do it but unfortunately the CoSMoS project ends with my PhD and it can’t be extended for 10 weeks. In my opinion, the most important requirement from an applicant is that they know about microcontrollers, can code in C and can design circuits. An intern which knows or has done more stuff would be nice, but it’s well worth applying for even if you don’t: It’s better to have a proactive and enthusiastic intern who’ll need to do some extra learning than someone who has the knowledge but won’t do anything.
Full job description and application details are below.
Continue reading Come work for us!
Another CoSMoS paper is being presented at the ALife XII conference which takes in Odense in August.
The paper is related to my PhD work, and in particular what I started out doing, back in the mists of time, relating to Chris Alexander’s Nature of Order. Chris is an architect, of the building variety, and this work relates to his ideas about how the built environment should, and in some cases does, evolve to preserve some properties he thinks important.
In a nutshell,we built a bit of software that places blobs in a two-dimensional space in a manner that attempts to mimic some of Chris’s properties. In particular ones that he calls Positive Space and Levels of Scale. The idea is that, to some extent, the diagrams look rather like city plans, especially the ones for ancient cities that have arisen from geography, geology and centuries [...]
I have recently returned from a very engaging visit with my collaborator in San Diego. Through my PhD we’ve had a good run at investigating EAE (a mouse proxy for multiple sclerosis, which he investigates in the lab) through modelling and simulation. Alas, PhD is nearing its 3 year deadline, and so we are looking into alternative funding opportunities to continue the work. We talked a lot about calibration of the simulation, and as such I’m currently performing a literature survey of modelling/simulation based biological research. Simulation offers a great deal of flexibility to the researcher, in computer code it is very easy to turn on and off molecule expressions that might be quite difficult to engineer into, say, a mouse. But, this raises an interesting question: with all this power to represent whatever might take one’s fancy, how can you be sure that your simulation [...]
Science is, in theory, “nice and principled”. We have the scientific method to guide experimentation, we have null hypothesis testing, so one could say nothing can really go wrong. What we get is what is “out there”. That is in theory, of course…
Coming back to good-old reality, things are not exactly perfect. First of all, we have the omnipresent sampling effect – our certainty over particular experimental results is always limited by measurement errors. Secondly, the way data is aggregated and conclusions drawn, may be biased: assumptions taken for granted, methods that are not really applicable to the given context, etc. Transparency is an issue. Finally, one of the biggest fish in the pond is the issue of scaling: drawing conclusions for large-scale systems, from small-scale observations.
From all this cloud of dust, argumentation techniques come to the rescue. They have been around for some time, especially in the domain [...]
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