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SEMINARS - Spring 2011

[Talk Schedule] [Abstracts] [Previous Seminars]

The Department of Computing Science and Mathematics presents the following seminars. Unless otherwise stated, seminars will take place in Room 4B94 of the Cottrell Building, University of Stirling from 15.00 to 16.00 on Friday afternoons during semester time. For instructions on how to get to the University, please look at the following routes.

If you would like to give a seminar to the department in future or if you need more information, please contact the seminar organiser, Marwan Fayed, by

or sending email to email.png.)

Talk Schedule [Top] [Abstracts]

18th Feb, 3-4pm
Professor Ian Sommerville
Title: Design for Recover: New challenges for large scale IT systems.
St Andrews University
4th March, 3-4pm, Room 2X4
Stewart Waterson
Title: Digital Game Competition for Glasgow 2014 Commonwealth Games.
Games Consultancy
9th March, 3-4pm
(New Date!) Professor David Marples
Title: Futurecar: Considering the future of the automobile in an environmentally constrained, connected, world.
Technolution BV
18th March 2:30-3:30pm
Rune Fensli (Note the time!)
Title: TBA
University of Agder, Norway
25th March, 3-4pm
(New Date!) Ekaterina Komendantskaya
Title: Computational Logic in Artificial Neural Networks
University of Dundee
8th April, 3-4pm
Jeremy Singer
Title: TBA
University of Glasgow
Title: TBA

9th March [Schedule]
Futurecar: Considering the future of the automobile in an environmentally constrained, connected, world.
Professor David Marples
Technolution BV
Our next generation of transport does not have the freedom to consume the earth's resources that previous generations have.In this seminar we consider the impact that environmental constraints are having on the vehicles in design right now and how some of the challenges are being met by individual vehicles communicating to each other and back end systems.
18th February [Schedule]
Design for Recover: New challenges for large scale IT systems
Professor Ian Sommerville
University of St Andrews
Since the 1980s, the object of design for dependability has been to avoid, detect or tolerate system faults so that these do not result in failures that are detectable outside the system. Whilst this is potentially achievable in medium size systems that are controlled by a single organisations, it is now practically impossible to achieve in large-scale systems of systems where different parts of the system are owned and controlled by different organisations. Therefore, we must accept the inevitability of failure and re-orient our system design strategies to recover from those failures at minimal cost and as quickly as possible. This talk will discuss why such recovery strategies cannot be purely technical but must be socio-technical in nature and argue that design for recovery will require a better understanding of how people recover from failure and the information they need during that recovery process. I will argue that supporting recovery should be a fundamental design objective of systems and explore what this means for current approaches to large-scale systems design.
25th March [Schedule]
Computational Logic in Artificial Neural Networks
Ekaterina Komendantskaya
University of Dundee
The most successful attempts to merge logic and neural networks have formed two disciplines in AI. One field - called statistical relational learning - is focused on the statistical data, while considering the logical (or relational) knowledge as a meta property arising from the statistical or probabilistic models. The second field - neuro-symbolic integration - comes to the problem from the point of view of formal logic, and looks for machine learning methods or neurocomputing methods that can be useful for the purposes of logical inference.

It is common for neuro-symbolic systems to propose unconventional models of neural networks to solve conventional logic problems. In contrast, the statistical relational learning would generally tend to use conventional learning methods, while the relational (or logic) level of reasoning would be somewhat different from the mainstream methods used in computational logic.

In this talk, I will consider several examples of recently proposed neuro-symbolic systems, and analyse their capacity to do either logic inference or learning (or both) along the lines above.

Previous Seminar Series [Top] [Abstracts] [Schedule]

2010 - Spring 2009 - Spring Autumn
2008 - Spring Autumn
2007 - Spring Autumn
2006 - Spring Autumn
2005 - Spring Autumn
2004 - Spring Autumn
2003 - Spring Autumn


Last Modified: 18th February 2010