Intelligent Systems

Presentation

Local search algorithms (Hill-Climbing, Simulated Annealing, Tabu search)

Algorithmes évolutionnaires (Genetic Algorithms, Ant Colony Optimization)

Hybrid Metaheuristics

Objectives

This course is heterogeneous since it groups together three parts :

• Artificial Intelligence and Problem Solving (AI-PS)
• Semantic Web (SW)
• Meta-heuristics (MH)

At the end of this module, students are expected to be able to …

[AI-PS]

Develop programs that integrate

-       A* algorithm for searching the best action plan in a problem-state space

-       Algorithm AO* for problem decomposition graphs (and/or graphs, hypergraphs)

-       Algorithms for 2 players games : minmax, negamax, alphabeta,

[SW part]

Define the major issues of the semantic web.

Understand the RDF graph model and its use for describing web resources and their metadata.

Design ontologies for knowledge representation, with the OWL language.

Develop an application that access to some ontology and infers new knowledge through a reasoning.

[MH Part]

Know the main classes of discrete decision problems and optimization problems.

Apply three main classes of metaheuristics :

- Local search methods

- Evolutionary methods

- Hybrid methods

Form of assessment

The evaluation of outcome prior learning is made as a continuous training during the semester. According ot the teaching, the assessment will be different: as a written exam, an oral exam, a record, a written report, peers review...