Program and Evaluation Process


The course features both regular lectures and lab sessions. For each of the considered topics, a lab session will be scheduled after the lectures, so as to provide a chance to learn the software systems related to the presented topic. The techniques discussed in this course represent the state of the art of scientific research in Artificial Intelligence: for each of the consdidered topic, the students will be referred to survey paper that provide a good overview of state of the art research.

The detailed program is as follows:

  1. Planning
    1. Non linear planning
    2. Hierarchical planning
    3. Graph-based planning 
  2. Swarm Intelligence
    1. Ant-colony Optimization
    2. Particle Swarm Optimization
  3. Constraint Programming and Optimization
    1. Applucations
    2. Advanced Search and Propagation Strategies
  4. Machine Learning
    1. Decision Trees
    2. Neural Networks

Assessment methods

The final exams consts of a written test containing both exercises and questions about theoretical topics. For this reason, it will not be possible to bring notes and books to the test.

Optional Course Project

It is possible to opt (by formally requesting it when planning the courses to take) for a course project in Intelligent Systems. In such a case, the project topic should be defined together with the course teacher.

The project may involve using an existing system to solve a complex problem , or the development of a new tool to solve for an AI application. The student should provide:

  • An accurate report about the project contents and the developed code
  • A presentaion (i.e. slides) to summarize the main steps of the projec, which will serve as a basis to guide the discussion
  • The project code