Search    Print

Intelligenza Artificiale
ARTIFICIAL INTELLIGENCE
Aldo Franco Dragoni

Seat Ingegneria
A.A. 2016/2017
Credits 9
Hours 72
Period I
Language ENG

Prerequisites
None

Learning outcomes
KNOWLEDGE AND UNDERSTANDING:
The course objective is to provide a broad overview of the concepts and methods that traditionally are grouped under the designation of "Artificial Intelligence". More emphasis is given to logic-based approaches, that is the methods that tend to replicate in the machines the logical inference mechanisms of human thought, and problem solving techniques based on research in a state space (with and without heuristics). As a programming tool theoretical and practical teaching of logic programming with constraints is provided.
CAPACITY TO APPLY KNOWLEDGE AND UNDERSTANDING:
The student will be able to represent knowledge and to design "smart applications" based on logical reasoning and research in a state space.
TRANSVERSAL SKILLS:
The knowledge provided during the course is completely oriented to "problem solving" and therefore to the ability to solve complex problems through the synthesis of suitable resolution algorithms. The study of symbolic logic helps to acquire a "mindset" that enables the student to formalize the problems correctly.

Program
Introduction to Artificial Intelligence Problem solving with Search. Euristic Search. Constraint Satisfaction Problems Adversary Search and Games Knowledge Representation and Reasoning Uncertain Reasoning and Fuzzy Logic First Order Logic Logic Programming PROLOG AI applications in PROLOG

Development of the examination
LEARNING EVALUATION METHODS
The examination is held in two tests: a test of programming in PROLOG and a written exam with exercises of Artificial Intelligence to solve with the help of textbooks and class notes

LEARNING EVALUATION CRITERIA
The evaluation focuses on the practice: the student must demonstrate the ability to program in PROLOG and knowing how to use the ideas of artificial intelligence to solve concrete problems.

LEARNING MEASUREMENT CRITERIA
Both tests are evaluated thirty

FINAL MARK ALLOCATION CRITERIA
The final evaluation is the average of thirty between the two assessments reported

Recommended reading
Russel, Norvig "Intelligenza Artificiale-un approccio moderno" Pearson Sterling, Shapiro "L'arte del Prolog" Hoepli

Courses

• Ingegneria Informatica e dell'Automazione (Corso di Laurea Magistrale (DM 270/04))