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

  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • Jensen, Richard; Shen, Qiang (2009-08)
    There has been great interest in developing methodologies that are capable of dealing with imprecision and uncertainty. The large amount of research currently being carried out in fuzzy and rough sets is representative of ...
  • Price, Chris; Snooke, Neal; Lewis, Stuart (2006-06)
    Software support for the automotive electrical design process is vital, as many of the safety analysis tasks needing to be carried out, while complex, are repetitive and time consuming. Such support is required throughout ...
  • MacParthaláin, Neil Seosamh; Shen, Qiang (2010-12)
    Rough set theory (RST) has enjoyed an enormous amount of attention in recent years and has been applied to many real-world problems including data mining, pattern recognition, and intelligent control. Much research has ...
  • Liang, Shen; He, Jun (2010-07)
    The performance of Evolutionary Programming (EP) is affected by many factors (e.g. mutation operators and selection strategies). Although the conventional approach with Gaussian mutation operator may be efficient, the ...
  • Shen, Qiang; MacParthaláin, Neil Seosamh; Jensen, Richard (2007-07-23)
    Feature Selection (FS) is a technique for dimensionality reduction. Its aims are to select a subset of the original features of a dataset which are rich in the most useful information. The benefits include improved data ...
  • Wu, Wei; Shen, Qiang; Qu, Yanpeng; MacParthaláin, Neil Seosamh (IEEE Press, 2010-09)
    The assessment of mammographic risk analysis is an important issue in the medical field. Various approaches have been applied in order to achieve a higher accuracy in such analysis. In this paper, an approach known as ...
  • Department of Computer Science; Advanced Reasoning Group; Snooke, Neal (2010-12-16)
    International PCT Application

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