Artificial Intelligence

Artificial Intelligence
14 - 16 x 2 hours lesson + 14 x 2 hours exercises

Katharina Morik

The lecture is part of the post-graduate programme. It is obligatory for all students of computer science. Therefore, it is held once a year. A script of 223 pages offers the basic material for the lecture. The exercises are done in Prolog. Lessons and script cover the following topics:

  1. What is AI? the topics, the approaches; Turing test, chinese room
  2. Problem solving as search depth-, breadth- first search, hill-climbing, A* and/or trees, minimax, alpha-/beta-pruning
  3. Production systems interpreter strategies, RETE, expert systems
  4. Knowledge acquisition KADS, sloppy modeling
  5. Problem solving by theorem proving propositional logic: syntax, semantics, inference predicate logic: syntax, semantics, interpretation, Herbrand theorem, substitution, unification, resolution
  6. Knowledge Representation semantic networks, frames description logics: syntax, semantics, hybrid inference
  7. Natural language systems architecture of a NLS syntax: DCG, chart-parsing, Cocke-Younger-Kasami parser lexicon: word form clauses syntactic features and unification how to develop a grammar for an application semantics: reference, ambiguities, semantic representation languages
  8. Machine learning learning as search: version space, ID3 conceptual clustering (star method, UNIMEM) deductive learning ILP: theta-subsumption, generalized theta-subsumption learnability: identification in the limit (MIS), PAC-learning