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LINGI 2261 - Artificial Intelligence: representation and reasoning

Prof. Yves Deville ( )
Teaching assistants: François Aubry ( ) and Michael Saint-Guillain ( )
Reference book: Stuart Russell and Peter Norvig “Artificial Intelligence: A Modern Approach”, Third Edition, Prenticel Hall, 2010 (AIMA)

Learning outcomes
A student completing this AI course will be able to :

  • Understand and explain the basic knowldge representation, problem solving and reasonning methods in artificial intelligenceAssess the applicability, strength, and weaknesses of the basic knowledge representation, problem solving and reasonning in solving particular engineering problems
  • Develop intelligent systems by assembling solutions to concrete computational problems
  • Understand the role of knowledge representation, problem solving and reasonning in intelligent-system engineering

Course content

  • Problem solving by searching : formulating problems, uninformed and informed search strategies, local search, evaluation of behavior and estimated cost, applications * Constraint satisfaction : formulating problems as CSP, backtracking and constraint propagation, applications
  • Games and adversarial search : minimax algorithm and Alpha-Beta? pruning, applications
  • Propositional logic : representing knowledge in PL, inference and reasoning, applications
  • First-order logic : representing knowledge in FOL, inference and reasining, forward and backward chaining, rule-based systems, applications
  • Planning : languages of planning problems, search methods, planing graphs, hierarchical planning, extensions, applications
  • AI, philosophy and ethics : can machines act intelligently, can machines really think, ethics and risks of AI, future of AI

AI Game Contest
A yearly contest where teams of students compete with AI agents. Follow the link to discover the previous editions.

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