SYMBOLIC ARTIFICIAL INTELLIGENCE

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

Error message

User error: Failed to connect to memcache server: druportbe01:11211 in dmemcache_object() (line 415 of /production/drupal/dim_prod/drupal/d7cl4/prod/unich/releases/7/web/sites/all/modules/contrib/memcache/dmemcache.inc).

General Module

Course Sheet

Course code:

000913LM2

Academic Year of enrolment:

2021

Year of supply:

2023/2024

Type of Course:

Elective Course

Degree:

Course of 1st Cycle Degree in ECONOMICS AND BUSINESS INFORMATICS

Disciplinary Sector:

Computer Science

Is part of:

ARTIFICIAL INTELLIGENCE

Location:

PESCARA

Credits:

Programme Year:

Professor and Collaborators:

MEO MARIA CHIARA

Cycle:

First semester

Hours of classroom activity:

Individual study hours:

Prerequisites:

none

Objectives

The course introduces the student to the principles and methods used to solve Artificial Intelligence methods, with a particular attention to knowledge-based systems and computational logic approaches.

Contents

Introduction to Artificial Intelligence.
Problem solving in AI.
Knowledge Representation.
Languages for Artficial Intelligence.

Extended Syllabus

Introduction to Artificial Intelligence: brief history of AI, main application fields, introduction to knowledge-based systems and architectural organization.
Problem solving in AI: representation through the notion of state, forward e backward reasoning, solving as a search and search strategies (informed and non). Games, constraint satisfaction problems, and planning problems.
Knowledge Representation: First Order Predicate Logic, Production Rules Systems, Knowledge-based systems, Some hints about formal ontologies.
Languages for Artficial Intelligence.

Recommended Bibliography

S. J. Russel, P. Norvig: "Artificail Intelligence: A modern approach", Prentice Hall, Last or previous edition.

Teaching Methods

Front lectures and exercises in classroom using blackboard, projector and
computer; practical activities (guided exercices).

Although recommended, course attendance is not mandatory.

Evaluation methods

Verification of learning:

Written exam.

Other courses

Course year: 1

FOUNDATIONS OF COMPUTER SCIENCE
9 Credits
PROGRAMMING AND ALGORITHMS 1
9 Credits
BUSINESS ADMINISTRATION
9 Credits - Basic
CALCULUS
9 Credits - Basic
MICROECONOMICS
9 Credits - Basic
PRIVATE LAW AND NEW TECHNOLOGY LAW
9 Credits - Basic
CERTIFICATE OF COMPETENCY IN TECHNICAL-SCIENTIFIC ENGLISH
6 Credits - Language/Final Examination

Course year: 2

DATABASES
6 Credits
PROGRAMMING AND ALGORITHMS 2
9 Credits
WEB PROGRAMMING
6 Credits
COMMERCIAL AND ECONOMIC LAW
9 Credits - Characterising
ECONOMIC GEOGRAPHY
6 Credits - Characterising
GAME THEORY
6 Credits - Characterising
INTERNET ECONOMICS
9 Credits - Characterising
LABOUR LAW
9 Credits - Characterising
MACROECONOMICS
9 Credits - Characterising
STATISTICS
9 Credits - Characterising

Course year: 3

ARTIFICIAL INTELLIGENCE
6 Credits
DATA MINING
6 Credits - Characterising
ECONOMETRICS
9 Credits - Characterising
FINANCIAL REPORTING
9 Credits - Characterising
ADVANCED PROGRAMMING
6 Credits - Related/Supplementary
INTERNET AND NETWORKING
6 Credits - Related/Supplementary
MOBILE SYSTEMS
6 Credits - Related/Supplementary
REINFORCEMENT LEARNING IN ARTIFICIAL INTELLIGENCE
6 Credits - Related/Supplementary
DEEPENING OF STATISTICS
9 Credits - Elective Course
ELECTIVE COURSES
18 Credits - Elective Course
IN-DEPTH STATISTICS
6 Credits - Elective Course
IN-DEPTH STATISTICS
3 Credits - Elective Course
FINAL EXAMINATION
3 Credits - Language/Final Examination
PROFESSIONAL SEMINARS
6 Credits

Search form

Aree

Error message

SYMBOLIC ARTIFICIAL INTELLIGENCE