Dr Xiuyi Fan

Research Highlights

1. Fan, X. (n.d.) A Temporal Planning Example with Assumption-Based Argumentation. In PRIMA 2018: Principles and Practice of Multi-Agent Systems (pp. 362-370).

   https://doi.org/10.1007/978-3-030-03098-8_22, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa46165
2. Zhong, Q., Fan, X., Luo, X., & Toni, F. (2019). An explainable multi-attribute decision model based on argumentation. Expert Systems with Applications, 117, 42-61.

   https://doi.org/10.1016/j.eswa.2018.09.038, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa44264
3. Fan, X. (2018). On Generating Explainable Plans with Assumption-Based Argumentation. PRIMA 2018: Principles and Practice of Multi-Agent Systems (pp. 344-361). The 21st International Conference on Principles and Practice of Multi-Agent Systems (PRIMA2018)

   https://doi.org/10.1007/978-3-030-03098-8_21, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa43698

   http://2018.prima-conference.org/
4. Fan, X. (2018). PRIMA 2018: Principles and Practice of Multi-Agent Systems The 21st International Conference on Principles and Practice of Multi-Agent Systems (PRIMA2018).

   https://doi.org/10.1007/978-3-030-03098-8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa43697

   http://2018.prima-conference.org/
5. Fan, X., Zhang, H., Leung, C., & Shen, Z. (2018). Fall Detection with Unobtrusive Infrared Array Sensors. Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System (pp. 253-267). Springer

   https://doi.org/10.1007/978-3-319-90509-9_15, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39574

All Research

• Explainable AI Techniques for Informed and Fair Decision Making (current)

  PhD

  Other supervisor: Dr Bertie Muller

• Developing a Unified Framework for Joining Ontological Knowledge and Patient Data «br /»«br /»«br /»«br /»«br /» for Improved Diagnosis and Treatment«br /»«br /»«br /»«br /»«br /» (current)

  PhD

  Other supervisor: Prof Gert Aarts

• Untitled (current)

  MSc

  Other supervisor: Prof Markus Roggenbach

• CSC325 Artificial Intelligence

  CSC325 is an introduction to Artificial Intelligence, focusing primarily on reasoning and problem-solving as a search for a solution rather than on statistical techniques for classification. The course may cover topics from amongst: search techniques; knowledge representation and expert systems; planning; scheduling; qualitative reasoning; language processing with grammar rules; and meta-programming.

• CSC364 Software Testing

  Testing is the process of systematically experimenting with an object (the SUT = System Under Test) in order to establish its quality, where quality means the degree of accordance to the intention or specification. This module will cover various test scenarios; practical exercises will allow the students to gain hands-on experience.

• CSCM23 Designing-in Trust, Understanding and Negotiation

  This module explores state-of-the-art methods and concepts to assist responsible design and development of technology with the aim of creating reliable and trusted systems. The content of this module will be delivered by expert lecturers and practitioners in the areas of trusted computation, bias and explainability in automated decision making and decision support, ethical considerations for AI, argumentation and negotiation, as well as formal methods, such as verification of critical systems.

• CSCM64 Software Testing

  Testing is the process of systematically experimenting with an object (the SUT = System Under Test) in order to establish its quality, where quality means the degree of accordance to the intention or specification. This module will provide an in-depth introduction to various test scenarios and enable students to gain hands-on experience by means of a number of practical exercises.