We are recruiting now (Lecturers, Senior Lecturers, Readers) with the deadline 3rd January 2014.
Please visit Jobs details
for on-line application. Or alternatively, please get in touch with us if you are interested to find out more.
Weiru Liu and Henri Prade (Guest Editors): Special Issue of Fuzzy Sets and Systems 2013.
Weiru Liu (Guest Editor): Special Issue of International Journal of Approximate Reasoning, 2013.
Conference Chairs/Steering Committee member:
Program Co-Chair of the 7th International Conference on Scalable Uncertainty Management
(SUM'13), 16-18 September, 2013. Washington DC Area, USA.
Conference/Program Chair of the Eleventh European Conference on Symbolic and Quantitative Approaches to
Reasoning under Uncertainty
(ECSQARU'11). June 29the - July 1st, 2011.
Belfast, Northern Ireland, UK.
1. Theories of Reasoning under Uncertainty, Uncertain Information Fusion, Conflict Analysis:
This research is concerned with modelling and merging uncertain information
in any intelligent systems. We particularly focus on the Dempster-Shafer theory of Evidence
(belief function theory), possibility theory and possibilistic logic, and probability theory.
We research into
the appropriateness of modelling uncertain information using these formalisms, aggregation
approaches offered by them, as well as conflict/inconsistency analysis among multiple
piece of uncertain information within these theories. Recent work has advanced to handling ambiguous evidence in game theory for security.
2. Theoretical aspects of Merging/Revising Uncertain and Inconsistent Knowledgebases:
Our research includes developing fusion methods (merging operators) and algorithms for merging
multiple knowledgebases (maybe with constrains), especially, possibilistic knowledgebases,
stratified knowledgebases, imprecise probabilistic logic based knowledge bases,
and heterogeneous uncertain information. We are also interested in developing
revision strategies/operators for revising above mentioned knowledge/belief bases. Research on developing approaches for
handling inconsistent knowledge bases has progressed to detecting inconsistencies in probabilistic knoweldge bases (learnd by other machine learning systems) and
repairing such inconsistencies.
3. Data mining, large scale data analytics, anomaly/threats detection
Knowledge acquisition is expensive and often there is no expert around from whom
to elicit the knowledge. We study Machine Learning and Data Mining techniques to discover
knowledge from data that are easily comprehensible to humans.
Our earliest work was on developing algorithms for constructing Bayesian Networks from data.
Recent works in this area are strongly influenced by emerging real-world applications, including:
Design and develop anomaly detection algorithms for detecting abnormal
behaviors in physical access control environment under the context of security
within CSIT; develop graph-theory based algorithms for identifying exercise
patterns and influences among participants in events; and discover social connection
patterns from social networks.
Design and develop various data analytical approaches, in collaboration with
Belfast City Council, for analyzing data on Pollution, Waste disposal/treatment, Recycling; Anti-Social Behaviors, etc.
Design and develop real-time threats and anomaly prediction
algorithms with missing values in datasets, using knowledge discovered above,
to provide real-time situation awareness for decision support.
4. Applications with our theoretical studies:
Numeric-based combination approaches to sensor data fusion for event-driven reasoning systems in large-scale sensor networks, multiple uncertain events
correlation in security using multi-agent platforms, uncertain activities correlation
in smart homes, combination of normal distributions with missing data in
and the combination of uncertain mappings from multiple ontology mapping
Logic-based merging approaches to inconsistent requirements fusion in
requirements engineering, inconsistency handling in description logic through merging
and revision, modelling
and merging XML documents with uncertain information, and merging and revision of
imprecise probabilistic knowledge for substrates prediction.