Artificial Intelligence

ECTS : 3

Lecturer in charge : Julien LESCA (https://sites.google.com/site/julienlesca/home)

Language of instruction : French

Teaching hours : 36

Course content:

 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.
   

Skills to be acquired:

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.

Assessment:

50% Project - 50% Exam

Bibliography, recommended reading:

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