General description
A problem at the heart of the research agenda for artificial intelligence is how to reason about the world. This module assesses the adequacy of models for this domain and aims to give students an understanding of the issues involved in effectively modelling and reasoning about systems. The course will be organised around a number of application domains and will show how the requirements of knowledge and software reuse lead to the need for both compositional models and multiple models of phenomena.

Aims
A problem at the heart of the research agenda for artificial intelligence is how to reason about the world. This module assesses the adequacy of models for this domain and aims to give students an understanding of the issues involved in effectively modelling and reasoning about the systems. The course will be organised around a number of application domains and these will be used to focus on specific problems that arise in modelling real systems, to show how existing techniques can be used and where such techniques prove inadequate.

Learning outcomes
On successful completion of this module students should:

• be capable of assessing the adequacy of models for effective reasoning;

• understand the consequences of different ontological choices;

• be aware of different models of behaviour, and methods for reasoning about change;

• understand how the requirements of knowledge and software reuse lead to the need for compositional modelling and multiple models of phenomena;

• understand the issues involved in automating the modelling process;

• appreciate how model-based reasoning can be used in diagnosis systems;

• be able to explain the use of MBR to identify the structure of systems.

Syllabus
1. Introduction - 1 Week
Revision of material in CS36110

2. QSIM: Its problems and successors - 1 Week
Approaches to the problem of spurious behaviours within constraint based ontology.

3. Fuzzy Qualitative Reasoning - 2 Weeks
FuSim and Mycroft and ongoing developments.

4. Modelling Issues - 2 Weeks
Compositional modelling, model properties, model switching, modelling methodology.

5. Functional Approaches - 1 Week
Reasoning about function, FMEA and functional labels, applications in design and diagnosis.

6. Model-based Diagnosis - 1 Week
Approaches to diagnosis, diagnostic methodology.

7. Learning of Models - 1 Week
Constraint-based learning of model structure from data.

8. Summary and Review - 1 Week