Syllabus

1. Introduction - General Course Info, AI Basics: AI systems, definitions, Relation to other courses especially last years AI I.

2. AI Revision - Summary of key concepts taught last year and examples of their use in this course.

3. Knowledge Representation - Representation approaches, Issues, Granularity.

4. Knowledge Representation - Rules and predicate logic, Semantic Nets and Frames: Weak slot and filler structures.

5. Knowledge Representation - Strong slot and filler structures: Conceptual dependency, scripts, CYC.

6. Reasoning with Uncertainty - Non-monotonic reasoning, problem solving.

7. Statistical Reasoning - Bayes Theorem, Certainty Factors, Bayesian Networks, Dempster Schafer Theory, Belief Models, Fuzzy models.

1. Parallel and Distributed Reasoning - Psychological modelling, Architectures, Blackboards.

2. Planning - Blocks world review, Components of a planning system, Goal stack planning.

3. Planning - Non linear planning, Constraint posting, Hierarchical planning, Reactive systems.

4. Understanding - What is understanding?, Why is it hard?, Understanding as constraint satisfaction.

5. Learning - What is learning?, Learning from advice, Learning in problem solving.

6. Learning - Learning from example, discovery and analogy. Genetic algorithms and Neural networks.

7. Common Sense - Qualitative physics, Common sense Ontologies, Memory organisation.

1. Vision - Image formation, describing images: surfaces, edges etc., 3D object modelling.

2. Vision - Line labelling, Relaxation labelling.

3. Vision - Object recognition, Template matching, Extended Gaussian images.

4. Vision - Object recognition: Model based matching - tree, graph searching and labelling methods.

5. Vision/Robotics - Shape from motion, Robotics planning.

6. Robotics - planning.