Advanced Reasoning Group

 

Advanced Reasoning

The Advanced Reasoning Group (ARG) aims to conduct innovative research in qualitative and approximative reasoning, including methods of knowledge representation, model generation and refinement, and model-based problem solving.

The ARG has an excellent track record of developing the scientific foundations necessary for building intelligent decision support systems, especially in crime detection and prevention, engineering design analysis, and computer-based diagnosis. In particular, the group is well known for its ground-breaking work on automated diagnostic and failure analysis for circuit design in the automotive industry, and it invention of fuzzy-rough semantics-preserving techniques for explicit knowledge model formulation and simplification. The group's research also includes other advanced computational intelligent techniques, e.g. evolutionary algorithms and meta-heuristics.

Research Topics

  • Multiple failure FMEA and sneak circuit analysis
  • Mode-based whole lifecycle automated system analysis
  • Qualitative model-based learning
  • Knowledge extraction over high dimensional data sets
  • Compositional modeling and preference handling.

Applications of the above techniques are wide-reaching, ranging from laboratory demonstrations (e.g. Metabolic pathway identification and simulation, and crime scenario construction and investigation) to commercial productions (e.g. automotive and aeronautical fault diagnosis, and consumer sensitive data analysis).

Recent Submissions

  • Price, Chris; Snooke, Neal (Research Publishing Services, 2008-04-03)
    The concept of software failure mode and effects analysis (FMEA) has grown in attractiveness over recent years as a way of assessing the reliability of software. Like its hardware counterpart, software FMEA is immensely ...
  • Eckerdal, Anna; Ratcliffe, Mark Bartley; Sanders, Kate; Moström, Jan Erik; Zander, Carol; McCartney, Robert (2006-09)
    This paper describes Threshold Concepts, a theory of learning that distinguishes core concepts whose characteristics can make them troublesome in learning. With an eye to applying this theory in computer science, we consider ...
  • Eckerdal, Anna; Ratcliffe, Mark Bartley; Sanders, Kate; Moström, Jan Erik; Zander, Carol; McCartney, Robert (2006-09)
    This paper describes Threshold Concepts, a theory of learning that distinguishes core concepts whose characteristics can make them troublesome in learning. With an eye to applying this theory in computer science, we consider ...
  • Downes, Clive; Snooke, Neal; Price, Chris; Aspey, Carol (2008-04-01)
    The ASTRAEA project is a £32M UK initiative to develop the safety case for unmanned aerial vehicles flying in commercial airspace. It is addressing both the issue of what needs to be covered by such a safety case, and how ...
  • Snooke, Neal; Bell, Jonathan (2002-06)
    Recent work in Model Based Reasoning has resulted in the development of automated tools to perform Failure Mode Effects Analysis (FMEA) and Sneak Circuit Analysis (SCA). These tools work at the component level for individual ...
  • MacParthaláin, Neil Seosamh; Shen, Qiang; Jensen, Richard (2006-09-04)
    Feature Selection (FS) methods based on fuzzy-rough set theory (FRFS) have employed the dependency function to guide the FS process with much success. More recently a method has been developed which uses fuzzy-entropy ...
  • MacParthaláin, Neil Seosamh; Jensen, Richard; Shen, Qiang (2008-06-01)
    Dataset dimensionality is undoubtedly the single most significant obstacle which exasperates any attempt to apply effective computational intelligence techniques to problem domains. In order to address this problem a ...
  • MacParthaláin, Neil Seosamh; Jensen, Richard; Shen, Qiang (2006-07-26)
    Feature Selection (FS) is a dimensionality reduction technique that aims to select a subset of the original features of a dataset which offer the most useful information. The benefits of feature selection include improved ...
  • Shen, Qiang; MacParthaláin, Neil Seosamh; Jensen, Richard (2008-09-10)
    The accuracy of methods for the detection of mammographic abnormaility is heavily related to breast tissue characteristics. A breast with high tissue density will have reduced sensitivity in terms of detection. Also, breast ...
  • Snooke, Neal; Price, Chris (1997-06-03)
    Qualitative reasoning about electrical systems has reached a level of achievement which allows it to be used for applications on realistic automotive circuits. The type of circuits for which it is most effective can be ...
  • Price, Chris; Snooke, Neal; Ellis, David (1999-01)
    Engineers have developed a number of design techniques in order to detect problems in their designs, such as yellow-boarding, FMECA, FTA and sneak circuit analysis. Concurrent engineering demands that all such design ...
  • Snooke, Neal (2010-08-04)
    This paper argues that software engineering should not overlook the lessons learned by other engineering disciplines with longer established histories. As software engineering evolves it should focus not only on application ...
  • Snooke, Neal (2004-06)
    Failure Mode and Effects Analysis is widely used in engineering hardware systems to help in understanding the effects of potential failures and the faults that cause them to occur. The analysis is iterative leading to ...
  • Bell, Jonathan; Snooke, Neal; Price, Chris (2005-08)
    Functional modeling is in use for the interpretation of the results of model based simulation of engineered systems for design analysis, enabling the automatic generation of a textual design analysis report that expresses ...
  • Bell, Jonathan; Price, Chris; Snooke, Neal (2005-05)
    Description of system function is already in use as the basis of an approach to interpretation of the results of simulation in design analysis, allowing an automated design analysis tool to generate a textual report detailing ...
  • Bell, Jonathan; Snooke, Neal (2004-08)
    Functional modeling languages have been used to describe processes that react to discrete external events and remain in a constant state until another such event stimulates a change in system state, and are deficient in ...
  • Snooke, Neal; Shipman, Richard (Springer, 2001-12-11)
    Model based reasoning applied to electrical systems has matured over recent years resulting in deployment of commercial design analysis tools in the automotive industry [1, 41. These tools work at the component level on ...
  • Price, Chris; Snooke, Neal; Lewis, Stuart (2003-08)
    Much research in model-based reasoning has concentrated on the use of a single, usually qualitative, level of modeling. This is less than ideal for many engineering applications, where the amount of knowledge available ...
  • Snooke, Neal; Price, Chris; Ellis, David (2002-06)
  • Szczuka, Marcin; Kryszkiewicz, Marzena; Ramanna, Sheela; Jensen, Richard; Hu, Qinghua; Advanced Reasoning Group; Department of Computer Science (Springer, 2010)

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