Introduction to Artificial Intelligence

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Course title

Introduction to Artificial Intelligence

Lecture type


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Lecturers and associates

Course objectives

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.

Required reading

(.), by Russell, Stuart J; Norvig, Peter. Artificial Intelligence: A Modern Approach. Prentice-Hall, Inc., 2003.,
(.), Dalbelo Bašić, Bojana; Šnajder, Jan. Umjetna inteligencija: Zaključivanje uporabom propozicijske i predikatne logike – zbirka zadataka. Zagreb: FER, 2008.,

Minimal learning outcomes

  • Define the basic concepts of artificial intelligence
  • Distinguish between symbolic and connectivistic approaches to AI
  • Apply state search algorithms and biologically inspired optimization algorithms on basic problems
  • Solve basic problems using logic programming
  • Apply inference algorithms on basic logical problems
  • Compare among various approaches to representing uncertainty
  • Assess the applicability of different AI methods on a given AI problem
  • Apply the basic machine learning algorithms
  • Review the philosophical aspects of artificial intelligence
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