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.

7
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.

14
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.