Introduction to problem solving theory. Search space, problem modeling, constraints, the problem of how problems are solved. Basic concepts (representation, objective, evaluation function, definition of a search problem, neighborhoods and local optima, hill climbing methods). Traditional methods – Part I (exhaustive search, local search). Traditional methods – Part II (depth and breadth search, greedy algorithms, A* algorithm, general graph search algorithm, dynamic programming).
Introduction to Artificial Intelligence, Constraint Satisfaction, Knowledge Representation (Definition, Essentials, Evaluation Criteria, Procedural and Declarative View), First-Order Categorical Logic, Basic Concepts of Model Theory and Proof Theory, Propositional Form, Principle of Solving, Contradiction of Solving, Strategies resolution (parent selection, sentence elimination), Prolog language, Production rules (syntax, inference process, conflict resolution strategies), Uncertain knowledge representation (Bayes rules, certainty factors), Meaningful networks, Frameworks, Action planning, Intelligent agents.