Artificial Intelligence

Course Code: 
Credit Points: 

Course Outline

  • Introduction to Artificial Intelligence
  • Introduction to problem solving theory
  • Search space, problem modeling, constraints, problem solving
  • Basic concepts (representation, goal, evaluation function, definition of search problem, neighboring areas and local optimal points, hill climbing methods)
  • Traditional Methods - Part I (exhaustive search, local search)
  • Traditional Methods - Part II (depth-first and breadth-first search, greedy algorithms, algorithm A *, general search algorithm, dynamic programming)
  • Solving Constraint Satisfaction Problems
  • Knowledge Representation (Definition, Key Elements, Evaluation Criteria, Procedural and Declarative View)
  • First-order predicate logic, Basic concepts of model theory and proof theory, Clausal form, Resolution Principle, Resolution Refutation, Resolution Strategies
  • Logical Programming, Prolog Language
  • Rules and Production Systems
  • Representation of uncertain knowledge (Bayes rules, certainty factors)
  • Semantic networks, Frames
  • Planning
  • Intelligent agents

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