Academic Year 2019/2020

ARTIFICIAL INTELLIGENCE

Course code: 089214

CFU: 5

POLITECNICO DI MILANO, Leonardo Campus

Scuola di Ingegneria Industriale e dell’Informazione, Master of Science in Engineering for Computing Systems

Instructor: Francesco AMIGONI

Syllabus

Aims and learning outcomes

The goal of the course is to introduce the students to basic problems, models, and techniques of Artificial Intelligence (AI), and to enable them to model and solve specific AI problems. The course covers the most fundamental concepts, modelling approaches, and resolution methods of core AI, and also provides an introduction to the history of the discipline and to some philosophical issues involved. The teaching method is traditional (classroom lessons).

Syllabus

Introduction to AI

• overview of the problems tackled in AI
• main research areas and application fields

State space and related problem solving methods

• state spaces and search methods
• non-informed and informed search methods
• adversarial search: minimax, alfa-beta pruning, and Monte Carlo tree search
• constraint satisfaction problems

Logic and reasoning

• recalls of propositional and first order logic
• recalls of resolution theorem proving
• model-checking methods for propositional logic, SAT solvers

Planning

• plan formation and execution
• the STRIPS/PDDL model
• planning as a search problem
• satisfiability-based planning (SATPlan)

History and foundations

• historical outline of the discipline
• critical concepts of AI and their philosophical implications

Prerequisites

Important prerequisites are computer programming, software engineering, databases, computability and complexity, and elements of propositional and first order logic.

Further information

For further information about the course: http://home.deib.polimi.it/amigoni/ArtificialIntelligence.html

Readings

Suggested readings

S. Russell, P. Norvig, “Artificial Intelligence: A Modern Approach (3rd edition)”, Prentice Hall, 2009, ISBN: 978-0136042594 (http://aima.cs.berkeley.edu/index.html).

There is also an Italian translation of the first part of the book that covers the topics of the course:

S. Russell, P. Norvig, “Intelligenza artificiale: un approccio moderno (terza edizione) volume 1”, Pearson, 2010, ISBN: 978-8871925936

Other teaching material

Further teaching material may be made available along the course.

Exam

The assessment is based on a written, closed-book test at the end of the course. It typically consists of four questions on the main topics of the course (state space search, adversarial search, constraint satisfaction problems, reasoning, and planning). Every test includes conceptual questions and exercises requiring a significant modelling effort. The test assigns a maximum of 32 points (30 cum laude is assigned when the total score is 31 or higher). Students have a maximum of 2 hours to answer the questions of the test. Students can take the test at any exam session during the year.