Artificial Intelligence and Computer Vision 2019

Exam topics for Artificial Intelligence

1. Basic concepts: history, goals of AI, Turing test, early systems, puzzles, "toy problems"
2. State space representation
   1. Searching the state space: greedy methods (and their problems), simulated annealing, tentative and irrevocable strategies
   2. Backtracking search, recursive implementation
   3. Graph searching
      1. "Blind" methods: breadth-first, depth-first, iterative deepening search
      2. Cost-based methods: uniform-cost and best-first
      3. The A* algorithm, properties, variants
      4. Constructing heuristic functions
   4. Searching for two-person games: the minimax algorithm, alpha-beta cuts
   5. Constraint satisfaction problems, local constraint checking, arc consistency, constraint propagation
3. Logic-based representation
   1. Propositional calculus
      1. Atomic propositions, logical connectives.
      2. Semantics of the propositional sentences. Interpretations. Evaluating formulas.
   2. Prolog
      1. Simple facts. Facts with arguments. Rules.
      2. Answering questions. Variables and unification. Search. Recursion.
      3. Lists.
4. Probabilistic representation
   1. Basic concepts: prior probability, probability axioms, random variables, joint probability distribution, conditional probability, Bayes' rule
   2. Probabilistic belief networks: construction, reasoning
   3. Making simple decisions: preferences and utility functions, the MEU principle, the value of information
   4. Sequential decision problems: Markov decision processes, agent's policy, state sequence utilities, discounting, policy evaluation, the value iteration algorithm
5. Action planning
   1. Classical action planning task
   2. Search and logic-based approaches
   3. STRIPS and ADL representation, the PDDL language
   4. Planning in the plan space, the POP algorithm

Monday, 28-Jan-2019 21:29:34 CET