**Lecture Component ** Artificial Intelligence** **

**Lecturer **TW/KW

**Hours ** 15 Lecture hours with 2 problem classes

1 **Historical Overview** - Definition of artificial intelligence (AI).Application areas. General problem solving versus specific knowledge. Complexity.

2-6 **Heuristic Search** - Uninformed versus informed search strategies. Formal properties of A*. Minimax game search, alpha-beta pruning.

7-10 **Logic and Resolution** -** **Knowledge representation. Propositional and predicate calculus. Inference rules. Clause form. Resolution strategies. Prolog and logic programming.

11-15 **Uncertainty Reasoning** - Probabilistic reasoning and Bayes theorem. Belief networks. Dempster-Shafer theory. Fuzzy logic.

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**Lecture Component **Neural Networks and** **AI Programming**
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**Lecturer ** TW/KW

**Hours ** 15 lecture hours with interspersed Problem Classes

1-2 **Theory of logic programs** - facts, queries, logical variables, recursion, rules, Horn clauses, structured data.

3-4 **Basic Prolog** - execution model, declarative and procedural meaning, backtracking, arithmetic, list representation, negation as failure and difficulties, simple examples.

5-7 **Prolog Programming and Techniques** - input/output, meta-logical and extra-logical predicates, set predicates, cuts, program development and style, correctness and completeness, Applications

8-10 ** ****Multi-Layer Perceptrons** - Convergence theorem, non-separability, LMS algorithms, steepest

descent, back-propagation, generalisation, learning factors.

11 **Radial Basis Function Networks **- Multivariable interpolation, regularisation, comparison with

MLP, learning strategies.

12-13 **Self-Organising Systems** - Hebbian learning, competitive learning, SOFM, LVQ

14-15 **Recurrent networks **- energy functions, Hopfield net, nonlinear dynamical systems, Liapunov stability, attractors.