Artificial Intelligence

Ects : 3

Enseignant responsable :

Volume horaire : 36

Description du contenu de l'enseignement :

This class introduces the main ideas and algorithms that let an artificial agent plan and carry out sequences of actions to reach a goal. We explore several families of techniques:

  • Search algorithms: How an agent explores a state space. We look at uninformed methods like breadth-first and depth-first search, and informed methods such as greedy best-first search and A*, which use heuristics to guide the search.
  • Local search and optimization: Techniques for improving a solution step by step, including hill climbing, simulated annealing, local beam search, and genetic algorithms.
  • Constraint satisfaction problems (CSPs): A framework for modelling problems using variables and constraints. We cover AC-3 for constraint propagation and backtracking search for finding consistent assignments.
  • Nondeterministic and partially observable environments: How agents plan when actions have uncertain effects or when they cannot see the full state of the world. We introduce AND–OR tree search and belief states.
  • Multi-agent environments: Basic ideas from game playing, including minimax and alpha–beta pruning, where agents must reason about opponents.
  • Classical planning: An introduction to PDDL and planning-graph techniques for encoding and solving high-level planning tasks.

Compétence à acquérir :

By the end of this course, students will be able to:

  • Model a variety of decision and planning problems using appropriate representations (state spaces, constraints, planning formalisms, etc.).
  • Understand and select suitable algorithmic approaches—such as search, optimization, or constraint-based methods—to compute solutions.
  • Use existing algorithms and solvers effectively, and assess their strengths, limitations, and suitability for a given problem.

Mode de contrôle des connaissances :

50% Project - 50% Exam

Bibliographie, lectures recommandées

Russell, S., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall.

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