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Introduction to artificial intelligence

Associate teachers


ECTS credits


Number of hours: Lectures + Seminars + Exercises

45 / 0 / 15

Course objectives

There are many cognitive tasks that are simple for the human, but very difficult for the computer. Artificial intelligence (AI) tries to solve these kinds of problems. Traditional approach to AI is based on symbolic knowledge representation and reasoning as symbol manipulation. Biologically inspired connectionist approach simulates cognitive abilities of the brain.

The aim of the course is to introduce different AI approaches and give an overview of AI methods, including methods for knowledge representation, automatic reasoning, problem solving, learning, and optimization.

Enrolment requirements and/or entry competences required for the course


Learning outcomes at the level of the programme to which the course contributes

  • Apply theoretical knowledge of the fundamentals of the six core disciplines and their relationship within cognitive science.
  • Apply specific knowledge and skills from selected disciplines constituting cognitive science.
  • Employ diverse disciplinary tools in exploring and describing the nature of cognitive processes.
  • Apply AI tools in concrete tasks and practical contexts.

Course content (syllabus)

  • AI problems and applications. AI definitions and Turing test. Agents and environments.
  • State space search problem. Uninformed search (breadth-first, depth-first, depth-first with iterative deepening).
  • Heuristics and informed search (hill-climbing, generic best-first). Minimax search and alpha-beta pruning. Constraint satisfaction (backtracking and local search methods). A* search. Beam search.
  • Logic as a knowledge representation scheme (ontological and epistemological commitments). Formalizing natural language sentences in predicate logic. Resolution rule for propositional logic. Resolution rule for predicate logic.
  • Logic-based expert systems. Reduction to logic programming.
  • Description logics and ontologies. Semantic networks. Non-monotonic reasoning. Spatial-temporal reasoning.
  • Rule-based reasoning. Case-based and model-based reasoning. Planning. Rule-based expert systems.
  • Midterm exam.
  • Certainty factors. Fuzzy sets and fuzzy logic. Fuzzy logic inference (fuzzy propositions, fuzzy relations, and fuzzy implications). Fuzzy inference engines. Fuzzyfication and defuzzyfication.
  • Probabilistic frameworks (Bayesian networks, Markov networks). Bayes inference.
  • Machine learning tasks and applications. Machine learning approaches and paradigms. Naïve Bayes classifier. Decision trees (ID3, C4.5).
  • Environment, reward and value functions. Markov decision processes (MDP). Approximate dynamic programming methods (Q-learning).
  • Perceptron (learning paradigms, Hebbian learning, competitive learning, Boltzmann learning). Multilayer perceptron (error-backpropagation learning, credit-assignment problem, backpropagation through time).
  • Philosophical issues.
  • Final exam.

Student responsibilities

Class attendance, laboratory exercises

Required literature

  • Stuart J. Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, 2020.
  • Elaine Rich, Kevin Night: Artificial Intelligence, McGraw-Hill, 1990.
  • Rolf Pfeifer, Christian Scheier: Understanding Intelligence, MIT Press, 1999.

Optional literature

  • Editors of Scientific American, Understanding Artificial Intelligence, Grand Central Publishing, 2002.
  • Blay Whitby: Artificial Intelligence, Oneworld Publications, 2003.
  • George F. Luger: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Addison-Wesley, 2008.