To provide an understanding of the relationship between Expert Systems and the wider field of artificial intelligence.
| Term | Prerequisites | Core For | 2 | No specific requirements, but general experience of computing and competence in programming are required. Some familiarity with logic is also very desirable. | N/A |
· The nature of Expert Systems. Types of applications of Expert Systems; relationship of Expert Systems to Artificial Intelligence and to Knowledge-Based Systems.
· The nature of expertise. Distinguishing features of Expert Systems. Benefits of using an Expert System. Choosing an application.
· Theoretical Foundations.
· What an expert system is; how it works and how it is built.
· Basic forms of inference: abduction; deduction; induction.
· The representation and manipulation of knowledge in a computer. Rule-based representations (with backward and forward reasoning); logic-based representations (with resolution refutation); taxonomies; meronomies; frames (with inheritance and exceptions); semantic and partitioned nets (query handling).
· Basic components of an expert system. Generation of explanations. Handling of uncertainties. Truth Maintenance Systems.
· Expert System Architectures. An analysis of some classic expert systems. Limitations of first generation expert systems. Deep expert systems. Co-operating expert systems and the blackboard model.
· Building Expert Systems. Methodologies for building expert systems: knowledge acquisition and elicitation; formalisation; representation and evaluation. Knowledge Engineering tools .
| Weighting | No. Exam Questions | No. Courseworks | 100% examination | 5 | 0 |