Ing Ind - Inf (Mag.)(ord. 270) - CR (263) MUSIC AND ACOUSTIC ENGINEERING
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089214 - ARTIFICIAL INTELLIGENCE
Ing Ind - Inf (Mag.)(ord. 270) - MI (471) BIOMEDICAL ENGINEERING - INGEGNERIA BIOMEDICA
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089214 - ARTIFICIAL INTELLIGENCE
Ing Ind - Inf (Mag.)(ord. 270) - MI (481) COMPUTER SCIENCE AND ENGINEERING - INGEGNERIA INFORMATICA
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089214 - 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 heuristic search methods (Monte Carlo tree search)
constraint satisfaction problems
Logic and reasoning
recalls of propositional and first order logic
recalls of resolution theorem proving (for propositional logic)
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
S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach (3rd edition), Editore: Prentice Hall, Anno edizione: 2009, ISBN: 978-0136042594 http://aima.cs.berkeley.edu
Software utilizzato
Nessun software richiesto
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
Inglese