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Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2019/2020
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 089214 - ARTIFICIAL INTELLIGENCE
Docente Amigoni Francesco
Cfu 5.00 Tipo insegnamento Monodisciplinare

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing Ind - Inf (Mag.)(ord. 270) - MI (263) MUSIC AND ACOUSTIC ENGINEERING*AM089214 - ARTIFICIAL INTELLIGENCE
Ing Ind - Inf (Mag.)(ord. 270) - MI (471) BIOMEDICAL ENGINEERING - INGEGNERIA BIOMEDICA*AM089214 - ARTIFICIAL INTELLIGENCE
Ing Ind - Inf (Mag.)(ord. 270) - MI (481) COMPUTER SCIENCE AND ENGINEERING - INGEGNERIA INFORMATICA*AM089214 - ARTIFICIAL INTELLIGENCE

Obiettivi dell'insegnamento

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).


Risultati di apprendimento attesi

Dublin descriptors

Expected learning outcomes

Knowledge and understanding

Students will become acquainted to:

·    the types of problems that are tackled in AI

·    the types of applications that can profit from the use of AI-based solutions

·    basic AI modelling and problem-solving techniques

·    crucial moments in the history of AI

·    critical issues involved in the effort of mechanising intelligent processes

Applying knowledge and understanding

Students will learn to:

·     analyse and model problems according to different AI approaches

·     design specific problem-solving techniques as instances of general classes of techniques

·     evaluate the complexity of different modelling and problem solving techniques in connection with particular problems

Lifelong learning skills

Students will become able to appreciate the relevance and possibility of developing and applying AI methods in different application fields of industrial relevance

Students will be able to understand and critically evaluate AI systems deployed in different application scenarios


Argomenti trattati

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

Prerequisiti

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


Modalità di valutazione

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.

 

Type of assessment

Description

Dublin descriptor

Written test

Solution of numerical problems

·     solution of problems involving the application of known methods and techniques

Exercises focusing on design aspects

·     solution of problems involving original modelling

Theoretical questions on all course topics with open answer:

·     questions concerning fundamental conceptual aspects of AI models and methods  

1,2

 

 

 

1, 2, 5

 

 

1, 5


Bibliografia
Risorsa bibliografica obbligatoriaS. Russell, P. Norvig, Artificial Intelligence: A Modern Approach (3rd edition), Editore: Prentice Hall, Anno edizione: 2009, ISBN: 978-0136042594 http://aima.cs.berkeley.edu

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
30:00
45:00
Esercitazione
20:00
30:00
Laboratorio Informatico
0:00
0:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
0:00
0:00
Totale 50:00 75:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
schedaincarico v. 1.6.1 / 1.6.1
Area Servizi ICT
08/12/2019