Attention is being paid to basic artificial intelligence domains – search, representation,
uncertainty. Discussed are approaches, techniques, representation techniques and basic
algorithms. Besides classical algorithms search topic considers heuristics and
approximation as modelling strategies. The representation topic covers constraint
satisfaction, logical formalism and effective algorithms for logical inference. The
uncertainty topic refers to probabilistic inference, formalisms for decision processes and
approaches for uncertainty modeling. Algorithms used in practical artificial intelligence are
presented. Application of investigated artificial intelligence models in natural language
processing, vision, machine learning and robotics is discussed.
Outcome:
Equipped with theoretical knowledge and practical skills the students will be able to select the
algorithm that fits best for inference in a specific domain; to implement and tune artificial
intelligence algorithms; to select the appropriate representation of artificial intelligence
problem or domain model, as well as to design models with desired representation.
