• identify problems that can be solved with basic AI solution techniques


• design and implement efficient AI algorithms


• verify the performance of implemented algorithms

• Knowledge representation: propositional and first order logic, binary
  decision diagrams.
  
• Problem solving by searching. Uninformed search strategies:
  breadth-first, depth-first, depth-limited. Informed search strategies:
  best-first, A* search, beam search, and IDA*


• Local search and optimization. Hill-climbing search, simulated
  annealing.


• Game playing. Minimax search, alpha-beta pruning.


• Propositional problems. SAT-solving.


• Constraint satisfaction problems. Backtracking search, local search,
  maintaining arc-consistency, join-graphs, decomposition of problems.


• Configuration problems. Interactive configuration by constraint
  satisfaction or pre-compilation.


• Planning problems. Progression/regression planning, planning heuristics,
  partial-order planning, non-deterministic planning, multi-agent planning,
  scheduling.


• Machine learning. Reinforcement learning, Decision tree learning.

NB! In the introductory week, meaning from 29 August to 2 September 2005, exercises from 13:00 to 16:00 are cancelled. This means, that there will be only lectures from 9:00 to 12:00.

The course consists of lectures, exercise classes, and programming exercises.