Academic Year 2020/2021 - 2° Year - Curriculum Data Science
Teaching Staff Credit Value: 9
Scientific field: INF/01 - Informatics
Taught classes: 36 hours
Exercise: 24 hours
Laboratories: 12 hours
Term / Semester:

Learning Objectives

  • Artificial Intelligence

    Knowledge and understanding: Students will acquire basic knowledge about Intelligent Agents and their main features.
    Applying knowledge and understanding: students will be to able to apply the acquired knowledge in several fields such as: searching for solutions to hard combinatorial problems, games and decision theory, automated deduction and reasoning.
    Autonomia di giudizio (making judgements): Students will be able to evaluate the possibility of developing algorithms and intelligent systems to mechanize decisional processes in different application fields.
    Communication skills: students will acquire the necessary communication skills and appropriate linguistic skills to explain and clarify problems relative to intelligent systems and their applications.
    Capacità di apprendimento (learning skills): students will be able to adapt the acquire knowledge to new contexts as well and to understand the limits of applicability of artificial intelligence techniques


    Knowledge and understanding: the knowledge on the design and implementation of several search algorithm methodologies for solving very complex computational problems will be acquired.

    Applying knowledge and understanding: the skills necessary to tackle and solve complex problems will be acquired, together to a correct algorithmic analysis and a proper knowledge on the resolution methodology to be adopted.

    Making judgements: the student will be able to evaluate the best and most appropriate search methodology to be used in the context of solving any complex problem.

    Learning skills: the student will be able to understand the advantages and disadvantages of the several search intelligent systems, and, consequently, making the appropriate choice on the methodologies to design and apply in order to solve complex problems.

Course Structure


    Classroom-taught lessons, and Practical laboratory.

    Can be also included external seminars held by expert researchers on related topics.

    Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

    Learning assessment may also be carried out on line, should the conditions require it.

Detailed Course Content

  • Artificial Intelligence

    The course is divided into 2 main parts. First part on Problem Solving, and second part on Knowledge and Reasoning.

    FIRST PART: Problem Solving

    • Foundations and history of Artificial Intelligence
    • Intelligent Agents and classifications
    • Search and Problem Solving
    • Search in games
    • Constraint Satisfaction Problems
    • Search using Natural Computing Algorithms
    • SECOND PART: Knowledge, Reasoning and Learning

    • Logical agents and puzzles
    • First order logic and Inferences
    • Uncertainty and Probability
    • Decision making, Utility and value of information
    • Learning from examples

    The laboratory course will focus on the analysis and development on the several solving-problems algorithmic techniques, such as:

    • non-informed research strategies (BFS, Research at uniform cost, DFS, Research in depth 'limited, etc.);
    • informed search strategies (Greedy BFS, A *, heuristic search with limited memory, etc.);
    • local search strategies;
    • algorithmic techniques for game theory (Minimax, Alfa-beta, etc.);
    • algorithms for constraints problems (Coloring of maps, etc.);
    • exact methods;
    • metaheuristics and hyper-heuristics;

Textbook Information

  • Artificial Intelligence

    Required textbook is Artificial Intelligence, a modern approach, 3rd Edition, S. Russel, P. Norvig. Other material will be provided by the instructor in class.

    1. Artificial Intelligence, a modern approach, 3rdEdition, S. Russel, P. Norvig.
    2. Metaheuristics: From Design to Implementation, E.G. Talbi, 2009.
    3. Hybrid Metaheuristics: Powerful Tools for Optimization, C. Blum and G.R. Raidl, 2016.
    4. Other material will be provided during the lectures.